R Notebook
#=============================================================================== # GTEx Tissue-Level Modeling Pipeline # Purpose: Tissue-specific quality prediction using Lasso regression #===============================================================================
#—————————————————————————— # 1. Setup #——————————————————————————
# Load data once
if(!exists("merged_gtex")) {
merged_gtex <- readRDS(file.path(data_path, "processed/merged_gtex.rds"))
}#—————————————————————————— # 2. Within-Group vs Between-Group Analysis for Single Tissue #——————————————————————————
# Analyze a specific tissue
tissue_name <- "Esophagus"
tissue_data <- merged_gtex[tissue_main == tissue_name]
subtypes <- unique(tissue_data$tissue_sub)
if(length(subtypes) > 1 && nrow(tissue_data) >= 10) {
# Extract and scale features
feature_cols <- grep("^feature_", names(tissue_data), value = TRUE)
feature_matrix <- scale(as.matrix(tissue_data[, ..feature_cols]))
# Calculate distance matrix
dist_matrix <- dist(feature_matrix)
# Calculate within-group and between-group distances
within_distances <- list()
between_distances <- list()
for(subtype in subtypes) {
subtype_indices <- which(tissue_data$tissue_sub == subtype)
if(length(subtype_indices) > 1) {
# Within-group distances
subtype_dist <- as.matrix(dist_matrix)[subtype_indices, subtype_indices]
within_distances[[subtype]] <- subtype_dist[upper.tri(subtype_dist)]
# Between-group distances
other_indices <- which(tissue_data$tissue_sub != subtype)
if(length(other_indices) > 0) {
between_dists <- as.matrix(dist_matrix)[subtype_indices, other_indices]
between_distances[[subtype]] <- as.vector(between_dists)
}
}
}
# Calculate summary statistics
all_within <- unlist(within_distances)
all_between <- unlist(between_distances)
similarity_results <- list(
tissue = tissue_name,
subtypes = subtypes,
samples_per_subtype = table(tissue_data$tissue_sub),
summary = data.frame(
comparison = c("Within Subtype", "Between Subtypes"),
mean_distance = c(mean(all_within), mean(all_between)),
median_distance = c(median(all_within), median(all_between))
),
ratio = mean(all_between) / mean(all_within)
)
# PERMANOVA test
perm_test <- adonis2(dist_matrix ~ tissue_sub, data = tissue_data)
statistical_results <- list(
tissue = tissue_name,
permanova = perm_test,
r_squared = perm_test$R2[1],
p_value = perm_test$`Pr(>F)`[1],
significant = perm_test$`Pr(>F)`[1] < 0.05
)
# Create visualizations
pca_result <- prcomp(feature_matrix)
pca_plot <- ggplot(data.frame(PC1 = pca_result$x[,1], PC2 = pca_result$x[,2],
Subtype = tissue_data$tissue_sub),
aes(x = PC1, y = PC2, color = Subtype)) +
geom_point(alpha = 0.7, size = 3) +
theme_minimal() +
labs(title = paste("PCA of", tissue_name, "by Subtype"))
ggsave(file.path(output_path, paste0(tissue_name, "_subtype_pca.pdf")),
pca_plot, width = 10, height = 8, dpi = 300)
# UMAP visualization
set.seed(123)
umap_result <- uwot::umap(feature_matrix, n_neighbors = min(15, nrow(feature_matrix)-1))
umap_plot <- ggplot(data.frame(UMAP1 = umap_result[,1], UMAP2 = umap_result[,2],
Subtype = tissue_data$tissue_sub),
aes(x = UMAP1, y = UMAP2, color = Subtype)) +
geom_point(alpha = 0.7, size = 3) +
theme_minimal() +
labs(title = paste("UMAP of", tissue_name, "by Subtype"))
ggsave(file.path(output_path, paste0(tissue_name, "_subtype_umap.pdf")),
umap_plot, width = 10, height = 8, dpi = 300)
# Save results
saveRDS(similarity_results, file.path(results_path, paste0(tissue_name, "_distance_analysis.rds")))
saveRDS(statistical_results, file.path(results_path, paste0(tissue_name, "_statistical_tests.rds")))
}#—————————————————————————— # 3. Tissue-Level Lasso Modeling #——————————————————————————
# Set parameters
k_folds <- 5
targets <- c("SMRIN", "SMATSSCR")
tissues <- unique(merged_gtex$tissue_main)
all_results <- list()
# Process each target
for(target in targets) {
target_results <- list()
message(paste("\nProcessing target:", target))
# Process each tissue
for(tissue_name in tissues) {
message(paste("\nAnalyzing", tissue_name, "for", target))
# Extract and filter data
tissue_data <- merged_gtex[tissue_main == tissue_name]
tissue_data <- tissue_data[!is.na(tissue_data[[target]])]
if(nrow(tissue_data) < 10) {
message(paste("Insufficient samples for", tissue_name))
next
}
# Feature selection
feature_cols <- grep("^feature_", names(tissue_data), value = TRUE)
feature_vars <- apply(tissue_data[, ..feature_cols], 2, var)
valid_features <- feature_cols[feature_vars > 1e-10]
# Create cross-validation folds
set.seed(123)
folds <- createFolds(tissue_data[[target]], k = k_folds)
# Cross-validation
fold_results <- list()
all_fold_predictions <- data.frame()
for(i in 1:k_folds) {
# Split data
test_indices <- folds[[i]]
train_data <- tissue_data[-test_indices]
test_data <- tissue_data[test_indices]
# Feature selection - top 5% correlated
feature_correlations <- sapply(valid_features, function(col) {
cor(train_data[[col]], train_data[[target]], use = "complete.obs")
})
top_n_features <- ceiling(length(valid_features) * 0.05)
top_features <- names(sort(abs(feature_correlations), decreasing = TRUE))[1:top_n_features]
# Prepare matrices
x_train <- as.matrix(train_data[, ..top_features])
y_train <- train_data[[target]]
x_train_scaled <- scale(x_train)
x_mean <- attr(x_train_scaled, "scaled:center")
x_sd <- attr(x_train_scaled, "scaled:scale")
# Train model
cv_fit <- cv.glmnet(x_train_scaled, y_train, alpha = 1)
best_lambda <- cv_fit$lambda.min
lasso_model <- glmnet(x_train_scaled, y_train, alpha = 1, lambda = best_lambda)
# Make predictions
x_test <- as.matrix(test_data[, ..top_features])
x_test_scaled <- scale(x_test, center = x_mean, scale = x_sd)
predictions <- predict(lasso_model, x_test_scaled, s = best_lambda)
# Store predictions
fold_predictions <- data.frame(
fold = i,
sample_id = test_data$sample_id,
actual = test_data[[target]],
predicted = as.numeric(predictions)
)
all_fold_predictions <- rbind(all_fold_predictions, fold_predictions)
# Calculate metrics
rmse <- sqrt(mean((predictions - test_data[[target]])^2))
r_value <- cor(predictions, test_data[[target]])
r2 <- cor(predictions, test_data[[target]])^2
mae <- mean(abs(predictions - test_data[[target]]))
fold_results[[i]] <- list(
fold = i,
rmse = rmse,
r = r_value,
r2 = r2,
mae = mae,
lambda = best_lambda,
model = lasso_model,
features = top_features
)
}
# Calculate overall performance
pooled_rmse <- sqrt(mean((all_fold_predictions$predicted - all_fold_predictions$actual)^2))
pooled_r <- cor(all_fold_predictions$predicted, all_fold_predictions$actual) # <-- R, not R²
pooled_r2 <- cor(all_fold_predictions$predicted, all_fold_predictions$actual)^2
pooled_mae <- mean(abs(all_fold_predictions$predicted - all_fold_predictions$actual))
# Count feature selection frequency
all_selected_features <- unlist(lapply(fold_results, function(x) x$features))
feature_counts <- table(all_selected_features)
consistent_features <- names(feature_counts[feature_counts >= 3])
# Store results
tissue_model_result <- list(
tissue = tissue_name,
target = target,
performance = list(
pooled_rmse = pooled_rmse,
pooled_r = pooled_r,
pooled_r2 = pooled_r2,
pooled_mae = pooled_mae
),
fold_results = fold_results,
feature_counts = feature_counts,
consistent_features = consistent_features,
sample_size = nrow(tissue_data),
all_predictions = all_fold_predictions
)
target_results[[tissue_name]] <- tissue_model_result
# Create visualization for good models (using R > 0.55 which is approximately R² > 0.3)
# if(pooled_r2 > 0.3) { # use if R² is required
if(abs(pooled_r) > 0.55) {
scatter_plot <- ggplot(all_fold_predictions, aes(x = actual, y = predicted)) +
geom_point(alpha = 0.6, color = "darkblue", size = 2) +
geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") +
geom_smooth(method = "lm", se = TRUE, color = "blue") +
labs(
title = paste(tissue_name, "-", target, "Prediction"),
subtitle = paste("R =", round(pooled_r, 3), "RMSE =", round(pooled_rmse, 3)),
# subtitle = paste("R² =", round(pooled_r2, 3), "RMSE =", round(pooled_rmse, 3)), # use if R² is required
x = paste("Actual", target),
y = paste("Predicted", target)
) +
theme_minimal()
ggsave(file.path(output_path, paste0(tissue_name, "_", target, "_scatter.pdf")),
scatter_plot, width = 10, height = 6, dpi = 300)
}
}
all_results[[target]] <- target_results
saveRDS(target_results, file.path(results_path, paste0(target, "_model_results.rds")))
}G3;
Processing target: SMRIN
gG3;
Analyzing Skin for SMRIN
gG3;
Analyzing Adipose for SMRIN
gG3;
Analyzing Nerve for SMRIN
gG3;
Analyzing Muscle for SMRIN
gG3;
Analyzing Artery for SMRIN
gG3;
Analyzing Heart for SMRIN
gsaveRDS(all_results, file.path(results_path, "all_tissue_models_complete.rds"))#—————————————————————————— # 4. Model Performance Summary #——————————————————————————
# Create performance summary
create_performance_summary <- function(results, target_name) {
summary_df <- data.frame()
for(tissue in names(results)) {
tissue_result <- results[[tissue]]
if(is.null(tissue_result)) next
row <- data.frame(
Tissue = tissue,
Samples = tissue_result$sample_size,
RMSE = round(tissue_result$performance$pooled_rmse, 3),
R = round(tissue_result$performance$pooled_r, 3),
# R_squared = round(tissue_result$performance$pooled_r2, 3), # use if R² is required
MAE = round(tissue_result$performance$pooled_mae, 3),
Consistent_Features = length(tissue_result$consistent_features)
)
summary_df <- rbind(summary_df, row)
}
summary_df <- summary_df[order(abs(summary_df$R), decreasing = TRUE),] # Sort by absolute R
summary_df$Performance_Category <- cut(abs(summary_df$R), # Use absolute R for categories
breaks = c(-Inf, 0.316, 0.548, 0.707, Inf),
labels = c("Poor", "Moderate", "Good", "Excellent"))
# summary_df <- summary_df[order(summary_df$R_squared, decreasing = TRUE),] # Sort by R²
# summary_df$Performance_Category <- cut(summary_df$R_squared, # Use R² for categories
# breaks = c(-Inf, 0.1, 0.3, 0.5, Inf),
# labels = c("Poor", "Moderate", "Good", "Excellent"))
return(summary_df)
}
# Create summaries
rin_summary <- create_performance_summary(all_results[["SMRIN"]], "RIN Score")
autolysis_summary <- create_performance_summary(all_results[["SMATSSCR"]], "Autolysis Score")
# Save summaries
write.csv(rin_summary, file.path(results_path, "rin_model_summary.csv"), row.names = FALSE)
write.csv(autolysis_summary, file.path(results_path, "autolysis_model_summary.csv"), row.names = FALSE)
# Create comparison
model_comparison <- merge(
rin_summary[, c("Tissue", "R")],
autolysis_summary[, c("Tissue", "R")],
by = "Tissue",
suffixes = c("_RIN", "_Auto")
)
model_comparison$Better_Model <- ifelse(abs(model_comparison$R_RIN) > abs(model_comparison$R_Auto),
"RIN", "Autolysis")
# Create comparison when if required for R²
# model_comparison <- merge(
# rin_summary[, c("Tissue", "R_squared")],
# autolysis_summary[, c("Tissue", "R_squared")],
# by = "Tissue",
# suffixes = c("_RIN", "_Auto")
# )
# model_comparison$Better_Model <- ifelse(model_comparison$R_squared_RIN > model_comparison$R_squared_Auto,
# "RIN", "Autolysis")
write.csv(model_comparison, file.path(results_path, "model_comparison.csv"), row.names = FALSE)#—————————————————————————— # 5. Figure 3 - Model Performance Visualization #——————————————————————————
# Panel A & B: Performance bar plots
create_horizontal_barplot <- function(all_results, score_type, panel_label) {
r_values <- sapply(all_results[[score_type]], function(x) {
if(is.null(x)) return(0)
return(x$performance$pooled_r)
# return(sqrt(x$performance$pooled_r2)) # use when required for R²
})
plot_data <- data.frame(
Tissue = names(r_values),
R = r_values
)[order(-r_values), ]
plot_data$Tissue <- factor(plot_data$Tissue, levels = rev(plot_data$Tissue))
if(score_type == "SMRIN") {
color_gradient <- colorRampPalette(c("#E8F5E8", "#66cdaa", "#2E8B57"))(100)
} else {
color_gradient <- colorRampPalette(c("#FFF2E6", "#FC8D62", "#D94801"))(100)
}
bar_plot <- ggplot(plot_data, aes(x = R, y = Tissue, fill = R)) +
geom_bar(stat = "identity", width = 0.8) +
geom_text(aes(label = sprintf("%.3f", R), x = R/2),
hjust = 0.5, size = 3.5, fontface = "bold") +
scale_fill_gradientn(colors = color_gradient, guide = "none") +
theme_minimal() +
labs(title = panel_label, x = "Correlation Coefficient (R)", y = "") +
theme(
plot.title = element_text(size = 20, face = "bold", hjust = 0),
axis.title.x = element_text(size = 14, face = "bold"),
axis.text.y = element_text(size = 13, face = "bold")
)
return(bar_plot)
}
rin_barplot <- create_horizontal_barplot(all_results, "SMRIN", "A")
autolysis_barplot <- create_horizontal_barplot(all_results, "SMATSSCR", "B")
# Panel C: RIN scatter plots
create_scatter_panel <- function(all_results, top_tissues) {
plots <- list()
for(i in 1:3) {
result <- all_results[["SMRIN"]][[top_tissues[i]]]
if(is.null(result)) next
all_preds <- result$all_predictions
r_value <- result$performance$pooled_r
# r_value <- sqrt(result$performance$pooled_r2) # use when required for R²
rmse_value <- result$performance$pooled_rmse
p <- ggplot(all_preds, aes(x = actual, y = predicted)) +
geom_point(alpha = 0.7, color = "#66cdaa", size = 1.5) +
geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") +
geom_smooth(method = "lm", se = TRUE, color = "blue") +
ggtitle(paste0(top_tissues[i], " | R = ", round(r_value, 3))) +
theme_minimal()
plots[[i]] <- p
}
return(plots)
}
# Get top 3 tissues
all_r_rin <- sapply(all_results[["SMRIN"]], function(x) {
if(is.null(x)) return(0)
return(abs(x$performance$pooled_r)) # Use absolute value for sorting
# return(sqrt(x$performance$pooled_r2)) # use when required for R²
})
top_rin_tissues <- names(sort(all_r_rin, decreasing = TRUE))[1:3]
rin_scatter_plots <- create_scatter_panel(all_results, top_rin_tissues)
rin_grid <- grid.arrange(grobs = rin_scatter_plots, nrow = 1,
bottom = textGrob("Actual RIN Score", gp = gpar(fontsize = 18, fontface = "bold")),
left = textGrob("Predicted RIN Score", rot = 90, gp = gpar(fontsize = 18, fontface = "bold")),
top = textGrob("C", x = 0.02, just = "left", gp = gpar(fontsize = 18, fontface = "bold")))
# Panel D: Autolysis box plots
create_boxplot_panel <- function(all_results, top_tissues) {
plots <- list()
for(i in 1:3) {
result <- all_results[["SMATSSCR"]][[top_tissues[i]]]
if(is.null(result)) next
all_preds <- result$all_predictions
all_preds$actual_factor <- factor(all_preds$actual, levels = c(0, 1, 2, 3))
r_value <- sqrt(result$performance$pooled_r2)
rmse_value <- result$performance$pooled_rmse
p <- ggplot(all_preds, aes(x = actual_factor, y = predicted)) +
geom_boxplot(aes(fill = actual_factor), outlier.shape = NA) +
geom_jitter(width = 0.2, height = 0, alpha = 0.2, color = "#21908d") +
scale_fill_manual(values = c("0" = "#e0e0e0", "1" = "#bdd7e7",
"2" = "#9ecae1", "3" = "#6baed6")) +
ggtitle(paste0(top_tissues[i], " | R = ", round(r_value, 3))) +
theme_minimal() +
theme(legend.position = "none")
plots[[i]] <- p
}
return(plots)
}
all_r_auto <- sapply(all_results[["SMATSSCR"]], function(x) {
if(is.null(x)) return(0)
return(sqrt(x$performance$pooled_r2))
})
top_auto_tissues <- names(sort(all_r_auto, decreasing = TRUE))[1:3]
auto_boxplot_plots <- create_boxplot_panel(all_results, top_auto_tissues)
autolysis_grid <- grid.arrange(grobs = auto_boxplot_plots, nrow = 1,
bottom = textGrob("Actual Category", gp = gpar(fontsize = 18, fontface = "bold")),
left = textGrob("Predicted Score", rot = 90, gp = gpar(fontsize = 18, fontface = "bold")),
top = textGrob("D", x = 0.02, just = "left", gp = gpar(fontsize = 18, fontface = "bold")))
# Combine all panels
combined_barplots <- grid.arrange(rin_barplot, autolysis_barplot, ncol = 2)
combined_CD_plots <- grid.arrange(rin_grid, autolysis_grid, nrow = 2)
figure3_final <- grid.arrange(combined_barplots, combined_CD_plots, ncol = 2, widths = c(0.45, 0.55))
ggsave(file.path(output_path, "Figure3_Final_Complete.pdf"),
figure3_final, width = 20, height = 12, dpi = 600, bg = "white")#—————————————————————————— # 6. Figure 4 - Feature Importance #——————————————————————————
# Extract top features
extract_features <- function(results, target, top_n = 20) {
all_tissue_features <- data.frame()
for(tissue in names(results[[target]])) {
tissue_result <- results[[target]][[tissue]]
if(is.null(tissue_result)) next
if(!is.null(tissue_result$feature_counts)) {
features <- names(tissue_result$feature_counts)
counts <- as.numeric(tissue_result$feature_counts)
tissue_df <- data.frame(
feature = features,
count = counts,
tissue = tissue,
stringsAsFactors = FALSE
)
all_tissue_features <- rbind(all_tissue_features, tissue_df)
}
}
feature_summary <- all_tissue_features %>%
group_by(feature) %>%
summarize(total_count = sum(count)) %>%
arrange(desc(total_count)) %>%
slice(1:top_n)
feature_matrix <- all_tissue_features %>%
filter(feature %in% feature_summary$feature) %>%
reshape2::dcast(feature ~ tissue, value.var = "count", fill = 0)
return(list(summary = feature_summary, matrix = feature_matrix))
}
rin_features <- extract_features(all_results, "SMRIN")
auto_features <- extract_features(all_results, "SMATSSCR")
# Find common features
common_features <- intersect(rin_features$summary$feature, auto_features$summary$feature)
rin_only_features <- setdiff(rin_features$summary$feature, auto_features$summary$feature)
auto_only_features <- setdiff(auto_features$summary$feature, rin_features$summary$feature)
# Create heatmaps
create_heatmap <- function(feature_data, color_palette) {
feature_long <- reshape2::melt(feature_data$matrix, id.vars = "feature",
variable.name = "tissue", value.name = "count")
feature_long <- merge(feature_long, feature_data$summary[, c("feature", "total_count")], by = "feature")
feature_long$feature_label <- sub("feature_", "", feature_long$feature)
p <- ggplot(feature_long, aes(x = tissue, y = reorder(feature_label, total_count))) +
geom_tile(aes(fill = count), color = "white", size = 0.1) +
scale_fill_gradientn(colors = color_palette, name = "Selection\nFrequency") +
labs(x = NULL, y = "Feature ID") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 65, hjust = 1, size = 12, face = "bold"),
axis.text.y = element_text(size = 12, face = "bold"),
panel.grid = element_blank()
)
return(p)
}
rin_heatmap <- create_heatmap(rin_features,
colorRampPalette(c("white", "#66CDAA", "#006400"))(100)) +
ggtitle("A")
auto_heatmap <- create_heatmap(auto_features,
colorRampPalette(c("white", "#FC8D62", "#D94801"))(100)) +
ggtitle("B")
# Create Venn diagram
venn_plot <- ggplot() +
ggforce::geom_circle(data = data.frame(x = c(-1, 1), y = c(0, 0), r = c(2, 2),
category = c("RIN Features", "Autolysis Features")),
aes(x0 = x, y0 = y, r = r, fill = category),
alpha = 0.5, color = "black", size = 0.8) +
geom_text(data = data.frame(x = c(-1.65, 0, 1.65), y = c(0, 0, 0),
label = c(length(rin_only_features),
length(common_features),
length(auto_only_features))),
aes(x = x, y = y, label = label), size = 10, fontface = "bold") +
scale_fill_manual(values = c("RIN Features" = "#66CDAA", "Autolysis Features" = "#FC8D62")) +
theme_void() +
theme(legend.position = "none") +
coord_fixed() +
xlim(-3, 3) + ylim(-2, 2) +
ggtitle("C")
# Combine plots
heatmaps <- plot_grid(rin_heatmap, auto_heatmap, ncol = 2)
figure4 <- plot_grid(heatmaps, venn_plot, nrow = 2, rel_heights = c(2, 1.5))
ggsave(file.path(output_path, "Figure4_Feature.pdf"),
figure4, width = 14, height = 12, dpi = 600, bg = "transparent")#—————————————————————————— # 7. Additional Visualizations and Comparative Analysis #——————————————————————————
# Create comparative performance chart for top tissues
create_comparative_bar_chart <- function(rin_results, autolysis_results) {
common_tissues <- intersect(rin_results$Tissue, autolysis_results$Tissue)
comparison_data <- data.frame(
Tissue = common_tissues,
RIN_R = rin_results$R[match(common_tissues, rin_results$Tissue)],
Autolysis_R = autolysis_results$R[match(common_tissues, autolysis_results$Tissue)]
)
comparison_data$Average_R <- (abs(comparison_data$RIN_R) + abs(comparison_data$Autolysis_R)) / 2
comparison_data <- comparison_data[order(-comparison_data$Average_R), ]
top_tissues <- head(comparison_data, 15)
plot_data <- reshape2::melt(top_tissues[, c("Tissue", "RIN_R", "Autolysis_R")],
id.vars = "Tissue", variable.name = "Model", value.name = "R_value")
plot_data$Tissue <- factor(plot_data$Tissue, levels = top_tissues$Tissue)
comparison_plot <- ggplot(plot_data, aes(x = Tissue, y = R_value, fill = Model)) +
geom_bar(stat = "identity", position = "dodge", alpha = 0.8) +
geom_text(aes(label = sprintf("%.3f", R_value)),
position = position_dodge(width = 0.9), vjust = -0.3, size = 3) +
scale_fill_manual(values = c("RIN_R" = "#66CDAA", "Autolysis_R" = "#FC8D62"),
labels = c("RIN", "Autolysis")) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 11, face = "bold"),
plot.title = element_text(size = 16, face = "bold"),
legend.position = "top"
) +
labs(
title = "Model Performance Comparison Across Top Tissues",
subtitle = "Top 15 tissues ranked by average correlation coefficient",
x = "Tissue Type",
y = "Correlation Coefficient (R)",
fill = "Model Type"
)
ggsave(file.path(output_path, "comparative_performance_chart.pdf"),
comparison_plot, width = 14, height = 8, dpi = 300)
return(comparison_plot)
}
# Create correlation distribution analysis
create_correlation_distribution <- function(results_df, target_name) {
r_dist_plot <- ggplot(results_df, aes(x = abs(R))) +
geom_histogram(binwidth = 0.05, fill = "steelblue", color = "white", alpha = 0.8) +
geom_vline(aes(xintercept = mean(abs(R))),
color = "red", linetype = "dashed", size = 1.2) +
geom_vline(aes(xintercept = median(abs(R))),
color = "darkgreen", linetype = "dashed", size = 1.2) +
annotate("text", x = mean(abs(results_df$R)) + 0.08, y = max(table(cut(abs(results_df$R), 20))) * 0.8,
label = paste("Mean =", round(mean(abs(results_df$R)), 3)),
color = "red", fontface = "bold") +
annotate("text", x = median(abs(results_df$R)) - 0.08, y = max(table(cut(abs(results_df$R), 20))) * 0.6,
label = paste("Median =", round(median(abs(results_df$R)), 3)),
color = "darkgreen", fontface = "bold") +
theme_minimal() +
labs(
title = paste("Distribution of Correlation Coefficients:", target_name),
subtitle = paste("Analysis of", nrow(results_df), "tissue-specific models"),
x = "Absolute Correlation Coefficient |R|",
y = "Frequency"
) +
theme(
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 12, face = "bold")
)
ggsave(file.path(output_path, paste0(gsub(" ", "_", tolower(target_name)), "_correlation_distribution.pdf")),
r_dist_plot, width = 10, height = 7, dpi = 300)
return(r_dist_plot)
}
# Create enhanced scatter plots with residual analysis
create_enhanced_scatter_plots <- function(results, target_name, top_n = 6) {
all_r_values <- sapply(results, function(x) {
if(is.null(x)) return(0)
return(abs(x$performance$pooled_r))
})
top_tissues <- names(sort(all_r_values, decreasing = TRUE))[1:min(top_n, length(all_r_values))]
for(tissue in top_tissues) {
if(is.null(results[[tissue]])) next
predictions <- results[[tissue]]$all_predictions
r_value <- results[[tissue]]$performance$pooled_r
rmse_value <- results[[tissue]]$performance$pooled_rmse
predictions$residual <- predictions$predicted - predictions$actual
scatter_plot <- ggplot(predictions, aes(x = actual, y = predicted)) +
geom_point(alpha = 0.7, color = "#2E86AB", size = 2.5) +
geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed", size = 1) +
geom_smooth(method = "lm", se = TRUE, color = "darkblue", alpha = 0.3) +
labs(
title = paste(tissue, "-", target_name, "Prediction Model"),
subtitle = paste("R =", round(r_value, 3), "| RMSE =", round(rmse_value, 3)),
x = paste("Actual", target_name),
y = paste("Predicted", target_name)
) +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold"),
plot.subtitle = element_text(size = 12),
axis.title = element_text(size = 11, face = "bold")
)
ggsave(file.path(output_path, paste0("enhanced_", tissue, "_", gsub(" ", "_", tolower(target_name)), "_scatter.pdf")),
scatter_plot, width = 10, height = 8, dpi = 300)
}
}
# Execute comparative visualizations
comparison_chart <- create_comparative_bar_chart(rin_summary, autolysis_summary)
rin_distribution <- create_correlation_distribution(rin_summary, "RIN Score")
autolysis_distribution <- create_correlation_distribution(autolysis_summary, "Autolysis Score")
create_enhanced_scatter_plots(all_results[["SMRIN"]], "RIN Score", top_n = 6)
create_enhanced_scatter_plots(all_results[["SMATSSCR"]], "Autolysis Score", top_n = 6)#—————————————————————————— # 8. Figure 5: Model Performance vs Sample Size Analysis #——————————————————————————
create_sample_size_analysis <- function(rin_summary, autolysis_summary) {
combined_data <- rbind(
data.frame(
Tissue = rin_summary$Tissue,
Samples = rin_summary$Samples,
R_value = abs(rin_summary$R),
Model = "RIN"
),
data.frame(
Tissue = autolysis_summary$Tissue,
Samples = autolysis_summary$Samples,
R_value = abs(autolysis_summary$R),
Model = "Autolysis"
)
)
scatter_panel <- ggplot(combined_data, aes(x = Samples, y = R_value, color = Model)) +
geom_point(size = 3.5, alpha = 0.8) +
geom_smooth(method = "lm", se = TRUE, alpha = 0.3, size = 1.2) +
scale_color_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
theme_minimal() +
labs(
title = "A",
x = "Sample Size (n)",
y = "Correlation Coefficient |R|",
subtitle = "Relationship between sample size and model performance"
) +
theme(
plot.title = element_text(size = 20, face = "bold", hjust = 0),
plot.subtitle = element_text(size = 12),
legend.position = "top",
legend.title = element_text(size = 12, face = "bold"),
axis.title = element_text(size = 12, face = "bold")
)
combined_data$size_category <- cut(combined_data$Samples,
breaks = c(0, 50, 100, 200, 500, Inf),
labels = c("≤50", "51-100", "101-200", "201-500", ">500"))
bin_summary <- combined_data %>%
group_by(size_category, Model) %>%
summarise(
mean_r = mean(R_value, na.rm = TRUE),
se_r = sd(R_value, na.rm = TRUE) / sqrt(n()),
tissue_count = n(),
.groups = 'drop'
)
bin_panel <- ggplot(bin_summary, aes(x = size_category, y = mean_r, fill = Model)) +
geom_bar(stat = "identity", position = "dodge", alpha = 0.85, width = 0.7) +
geom_errorbar(aes(ymin = mean_r - se_r, ymax = mean_r + se_r),
position = position_dodge(width = 0.7), width = 0.25, size = 0.8) +
geom_text(aes(label = tissue_count),
position = position_dodge(width = 0.7),
vjust = -1.5, size = 3.5, fontface = "bold") +
scale_fill_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
theme_minimal() +
labs(
title = "B",
x = "Sample Size Categories",
y = "Mean |R| ± SE",
subtitle = "Performance stratified by sample size (numbers show tissue count)"
) +
theme(
plot.title = element_text(size = 20, face = "bold", hjust = 0),
plot.subtitle = element_text(size = 12),
legend.position = "none",
axis.title = element_text(size = 12, face = "bold")
)
figure5_combined <- grid.arrange(scatter_panel, bin_panel, ncol = 2, widths = c(1, 1))
return(figure5_combined)
}
analyze_sample_size_effects <- function(combined_data) {
rin_data <- combined_data[combined_data$Model == "RIN", ]
auto_data <- combined_data[combined_data$Model == "Autolysis", ]
rin_cor <- cor.test(rin_data$Samples, rin_data$R_value)
auto_cor <- cor.test(auto_data$Samples, auto_data$R_value)
return(list(rin_test = rin_cor, autolysis_test = auto_cor))
}
figure5_plot <- create_sample_size_analysis(rin_summary, autolysis_summary)
sample_size_stats <- analyze_sample_size_effects(rbind(
data.frame(Tissue = rin_summary$Tissue, Samples = rin_summary$Samples,
R_value = abs(rin_summary$R), Model = "RIN"),
data.frame(Tissue = autolysis_summary$Tissue, Samples = autolysis_summary$Samples,
R_value = abs(autolysis_summary$R), Model = "Autolysis")
))
ggsave(file.path(output_path, "Figure5_Sample_Size_Analysis.pdf"),
figure5_plot, width = 16, height = 8, dpi = 600, bg = "white")G2;H2;Warningh in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
for '≤50' in 'mbcsToSbcs': <= substituted for ≤ (U+2264)g#—————————————————————————— # 9. Top 5 Tissues Quality Overlay #——————————————————————————
# Get top 5 tissues for each score
get_top5_tissues <- function(target_name) {
r_values <- sapply(all_results[[target_name]], function(x) {
if(is.null(x) || is.null(x$performance$pooled_r2)) return(0)
# return(sqrt(x$performance$pooled_r2)) # use when required for R²
return(abs(x$performance$pooled_r)) # Use absolute R for ranking
})
r_values <- r_values[r_values > 0]
top5 <- sort(r_values, decreasing = TRUE)[1:5]
return(data.frame(
tissue = names(top5),
correlation_r = as.numeric(top5)
))
}
top5_rin <- get_top5_tissues("SMRIN")
top5_auto <- get_top5_tissues("SMATSSCR")
# Create overlay plots
create_simple_overlay <- function(tissue_name, score_type, color_palette) {
tissue_data <- merged_gtex[tissue_main == tissue_name]
if(nrow(tissue_data) < 10) {
return(ggplot() + theme_void())
}
# Get features and run UMAP
feature_cols <- grep("^feature_", names(tissue_data), value = TRUE)
feature_matrix <- as.matrix(tissue_data[, ..feature_cols])
feature_matrix <- scale(feature_matrix[, apply(feature_matrix, 2, var) > 1e-10])
set.seed(123)
umap_result <- umap(feature_matrix, n_neighbors = min(15, nrow(feature_matrix) - 1))
plot_data <- data.frame(
UMAP1 = umap_result[, 1],
UMAP2 = umap_result[, 2],
Score = tissue_data[[score_type]]
)
# Separate data with and without scores
data_with_scores <- plot_data[!is.na(plot_data$Score), ]
data_missing_scores <- plot_data[is.na(plot_data$Score), ]
# Get R value
tissue_result <- all_results[[score_type]][[tissue_name]]
r_value <- if(!is.null(tissue_result)) tissue_result$performance$pooled_r else 0
# r_value <- if(!is.null(tissue_result)) sqrt(tissue_result$performance$pooled_r2) else 0 # use when required for R²
p <- ggplot() +
geom_point(data = data_missing_scores, aes(x = UMAP1, y = UMAP2),
color = "lightgrey", alpha = 0.5, size = 1.5) +
geom_point(data = data_with_scores, aes(x = UMAP1, y = UMAP2, color = Score),
alpha = 0.7, size = 2) +
color_palette +
theme_minimal() +
labs(
title = tissue_name,
subtitle = paste("R =", sprintf("%.3f", r_value)),
x = "UMAP1", y = "UMAP2"
)
return(p)
}
# Define color palettes
rin_palette <- scale_color_viridis_c(name = "RIN\nScore", option = "viridis")
auto_palette <- scale_color_viridis_c(name = "Autolysis\nScore", option = "plasma")
# Create plots
rin_plots <- lapply(1:5, function(i) {
create_simple_overlay(top5_rin$tissue[i], "SMRIN", rin_palette)
})
auto_plots <- lapply(1:5, function(i) {
create_simple_overlay(top5_auto$tissue[i], "SMATSSCR", auto_palette)
})
# Combine panels
panel_a <- grid.arrange(grobs = rin_plots, nrow = 1,
top = textGrob("A", gp = gpar(fontsize = 20, fontface = "bold"),
x = 0.02, just = "left"))
panel_b <- grid.arrange(grobs = auto_plots, nrow = 1,
top = textGrob("B", gp = gpar(fontsize = 20, fontface = "bold"),
x = 0.02, just = "left"))
final_plot <- grid.arrange(panel_a, panel_b, nrow = 2)
ggsave(file.path(output_path, "Top5_Tissues_Quality_Overlay_AB.pdf"),
final_plot, width = 20, height = 12, dpi = 300, bg = "white")#—————————————————————————— # 10. Figure 6: Cross-Tissue Feature Consistency Analysis #——————————————————————————
create_feature_consistency_analysis <- function(all_results) {
extract_consistent_features <- function(target_results, min_selections = 3) {
tissue_features <- list()
for(tissue in names(target_results)) {
if(!is.null(target_results[[tissue]]$feature_counts)) {
features <- names(target_results[[tissue]]$feature_counts)
counts <- as.numeric(target_results[[tissue]]$feature_counts)
selected_features <- features[counts >= min_selections]
tissue_features[[tissue]] <- selected_features
}
}
return(tissue_features)
}
rin_tissue_features <- extract_consistent_features(all_results[["SMRIN"]])
auto_tissue_features <- extract_consistent_features(all_results[["SMATSSCR"]])
create_consistency_heatmap <- function(tissue_features, title, color_scheme) {
all_features <- unique(unlist(tissue_features))
all_tissues <- names(tissue_features)
selection_matrix <- matrix(0, nrow = length(all_features), ncol = length(all_tissues))
rownames(selection_matrix) <- all_features
colnames(selection_matrix) <- all_tissues
for(tissue in all_tissues) {
selected <- tissue_features[[tissue]]
selection_matrix[selected, tissue] <- 1
}
consistency_scores <- rowSums(selection_matrix) / ncol(selection_matrix)
top_features <- names(sort(consistency_scores, decreasing = TRUE))[1:25]
plot_matrix <- selection_matrix[top_features, ]
plot_data <- expand.grid(Feature = rownames(plot_matrix),
Tissue = colnames(plot_matrix))
plot_data$Selected <- as.vector(plot_matrix)
plot_data$Consistency_Score <- consistency_scores[plot_data$Feature]
plot_data$Feature_ID <- sub("feature_", "", plot_data$Feature)
heatmap_plot <- ggplot(plot_data, aes(x = Tissue, y = reorder(Feature_ID, Consistency_Score))) +
geom_tile(aes(fill = factor(Selected)), color = "white", size = 0.3) +
scale_fill_manual(values = c("0" = "white", "1" = color_scheme),
name = "Selected", labels = c("No", "Yes")) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 9, face = "bold"),
axis.text.y = element_text(size = 9),
panel.grid = element_blank(),
legend.position = "right",
plot.title = element_text(size = 16, face = "bold")
) +
labs(
title = title,
x = NULL,
y = "Feature ID"
)
return(heatmap_plot)
}
panel_a <- create_consistency_heatmap(rin_tissue_features, "A. RIN Model Feature Consistency", "#2E8B57")
panel_b <- create_consistency_heatmap(auto_tissue_features, "B. Autolysis Model Feature Consistency", "#D2691E")
calculate_stability_distribution <- function(tissue_features) {
all_features <- unique(unlist(tissue_features))
stability_scores <- sapply(all_features, function(feature) {
tissues_with_feature <- sum(sapply(tissue_features, function(x) feature %in% x))
return(tissues_with_feature / length(tissue_features))
})
return(stability_scores)
}
rin_stability <- calculate_stability_distribution(rin_tissue_features)
auto_stability <- calculate_stability_distribution(auto_tissue_features)
stability_comparison <- data.frame(
Feature = c(names(rin_stability), names(auto_stability)),
Stability = c(rin_stability, auto_stability),
Model = rep(c("RIN", "Autolysis"), c(length(rin_stability), length(auto_stability)))
)
panel_c <- ggplot(stability_comparison, aes(x = Stability, fill = Model)) +
geom_histogram(alpha = 0.75, bins = 15, position = "identity") +
geom_vline(data = stability_comparison %>% group_by(Model) %>% summarise(mean_stab = mean(Stability)),
aes(xintercept = mean_stab, color = Model), linetype = "dashed", size = 1.2) +
scale_fill_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
scale_color_manual(values = c("RIN" = "#2E8B57", "Autolysis" = "#D2691E")) +
theme_minimal() +
labs(
title = "C. Feature Stability Distribution",
x = "Stability Score (Fraction of Tissues)",
y = "Number of Features",
subtitle = "Distribution of cross-tissue feature consistency"
) +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12),
legend.position = "top",
axis.title = element_text(size = 11, face = "bold")
)
top_panels <- grid.arrange(panel_a, panel_b, ncol = 2)
figure6_combined <- grid.arrange(top_panels, panel_c, nrow = 2, heights = c(2, 1))
return(figure6_combined)
}
calculate_feature_overlap_stats <- function(all_results) {
extract_top_features <- function(target_results, n = 50) {
feature_counts <- list()
for(tissue in names(target_results)) {
if(!is.null(target_results[[tissue]]$feature_counts)) {
counts <- target_results[[tissue]]$feature_counts
for(feat in names(counts)) {
if(is.null(feature_counts[[feat]])) feature_counts[[feat]] <- 0
feature_counts[[feat]] <- feature_counts[[feat]] + counts[[feat]]
}
}
}
sorted_features <- sort(unlist(feature_counts), decreasing = TRUE)
return(names(sorted_features)[1:min(n, length(sorted_features))])
}
rin_top <- extract_top_features(all_results[["SMRIN"]])
auto_top <- extract_top_features(all_results[["SMATSSCR"]])
overlap_count <- length(intersect(rin_top, auto_top))
rin_unique <- length(setdiff(rin_top, auto_top))
auto_unique <- length(setdiff(auto_top, rin_top))
return(list(common = overlap_count, rin_unique = rin_unique, auto_unique = auto_unique))
}
figure6_plot <- create_feature_consistency_analysis(all_results)
overlap_stats <- calculate_feature_overlap_stats(all_results)
ggsave(file.path(output_path, "Figure6_Feature_Consistency.pdf"),
figure6_plot, width = 18, height = 14, dpi = 600, bg = "white")#—————————————————————————— # 11. Figure 7: Model Residual Analysis #——————————————————————————
create_comprehensive_residual_analysis <- function(all_results, top_n = 6) {
identify_top_tissues <- function(target_results, n) {
performance_scores <- sapply(target_results, function(x) {
if(is.null(x)) return(0)
return(abs(x$performance$pooled_r))
})
top_tissues <- names(sort(performance_scores, decreasing = TRUE))[1:n]
return(top_tissues[!is.na(top_tissues)])
}
top_rin_tissues <- identify_top_tissues(all_results[["SMRIN"]], top_n)
top_auto_tissues <- identify_top_tissues(all_results[["SMATSSCR"]], top_n)
create_residual_panel <- function(tissues, target, color, panel_title) {
residual_plots <- list()
for(i in 1:min(3, length(tissues))) {
tissue_name <- tissues[i]
result <- all_results[[target]][[tissue_name]]
if(!is.null(result) && !is.null(result$all_predictions)) {
predictions <- result$all_predictions
predictions$residual <- predictions$predicted - predictions$actual
r_value <- result$performance$pooled_r
residual_plot <- ggplot(predictions, aes(x = actual, y = residual)) +
geom_point(alpha = 0.7, color = color, size = 2) +
geom_hline(yintercept = 0, linetype = "dashed", color = "red", size = 1) +
geom_smooth(method = "loess", se = TRUE, color = "darkblue", alpha = 0.3) +
theme_minimal() +
labs(
title = paste(tissue_name, "(R =", round(r_value, 3), ")"),
x = if(i == 3) paste("Actual", ifelse(target == "SMRIN", "RIN", "Autolysis")) else "",
y = if(i == 1) "Residual" else ""
) +
theme(
plot.title = element_text(size = 12, face = "bold"),
axis.title = element_text(size = 10, face = "bold")
)
residual_plots[[i]] <- residual_plot
}
}
panel <- grid.arrange(grobs = residual_plots, nrow = 1,
top = textGrob(panel_title, gp = gpar(fontsize = 16, fontface = "bold")))
return(panel)
}
panel_a <- create_residual_panel(top_rin_tissues, "SMRIN", "#66CDAA", "A. RIN Model Residuals")
panel_b <- create_residual_panel(top_auto_tissues, "SMATSSCR", "#FC8D62", "B. Autolysis Model Residuals")
create_residual_distribution <- function() {
all_residuals <- data.frame()
for(target in c("SMRIN", "SMATSSCR")) {
for(tissue in names(all_results[[target]])) {
result <- all_results[[target]][[tissue]]
if(!is.null(result) && !is.null(result$all_predictions)) {
predictions <- result$all_predictions
residual_data <- data.frame(
tissue = tissue,
target = ifelse(target == "SMRIN", "RIN", "Autolysis"),
residual = predictions$predicted - predictions$actual,
abs_residual = abs(predictions$predicted - predictions$actual)
)
all_residuals <- rbind(all_residuals, residual_data)
}
}
}
distribution_plot <- ggplot(all_residuals, aes(x = abs_residual, fill = target)) +
geom_histogram(alpha = 0.75, bins = 30, position = "identity") +
geom_vline(data = all_residuals %>% group_by(target) %>% summarise(mean_res = mean(abs_residual)),
aes(xintercept = mean_res, color = target), linetype = "dashed", size = 1.2) +
scale_fill_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
scale_color_manual(values = c("RIN" = "#2E8B57", "Autolysis" = "#D2691E")) +
theme_minimal() +
labs(
title = "C. Distribution of Absolute Residuals",
x = "Absolute Residual Magnitude",
y = "Frequency",
fill = "Model Type",
subtitle = "Comparison of prediction errors across all tissue models"
) +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12),
legend.position = "top",
axis.title = element_text(size = 11, face = "bold")
)
return(distribution_plot)
}
create_performance_residual_relationship <- function() {
performance_residual_data <- data.frame()
for(target in c("SMRIN", "SMATSSCR")) {
for(tissue in names(all_results[[target]])) {
result <- all_results[[target]][[tissue]]
if(!is.null(result) && !is.null(result$all_predictions)) {
predictions <- result$all_predictions
perf_data <- data.frame(
tissue = tissue,
target = ifelse(target == "SMRIN", "RIN", "Autolysis"),
r_value = abs(result$performance$pooled_r),
rmse = result$performance$pooled_rmse,
mean_abs_residual = mean(abs(predictions$predicted - predictions$actual))
)
performance_residual_data <- rbind(performance_residual_data, perf_data)
}
}
}
relationship_plot <- ggplot(performance_residual_data, aes(x = r_value, y = mean_abs_residual, color = target)) +
geom_point(size = 3.5, alpha = 0.8) +
geom_smooth(method = "lm", se = TRUE, alpha = 0.3, size = 1.2) +
scale_color_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
theme_minimal() +
labs(
title = "D. Model Performance vs Residual Magnitude",
x = "Correlation Coefficient |R|",
y = "Mean Absolute Residual",
color = "Model Type",
subtitle = "Relationship between prediction accuracy and error magnitude"
) +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12),
legend.position = "none",
axis.title = element_text(size = 11, face = "bold")
)
return(relationship_plot)
}
panel_c <- create_residual_distribution()
panel_d <- create_performance_residual_relationship()
bottom_panels <- grid.arrange(panel_c, panel_d, ncol = 2)
figure7_combined <- grid.arrange(panel_a, panel_b, bottom_panels, nrow = 3, heights = c(1, 1, 1))
return(figure7_combined)
}
analyze_residual_patterns <- function(all_results) {
residual_stats <- data.frame()
for(target in c("SMRIN", "SMATSSCR")) {
for(tissue in names(all_results[[target]])) {
result <- all_results[[target]][[tissue]]
if(!is.null(result) && !is.null(result$all_predictions)) {
predictions <- result$all_predictions
residuals <- predictions$predicted - predictions$actual
stats_row <- data.frame(
tissue = tissue,
target = target,
mean_residual = mean(residuals),
sd_residual = sd(residuals),
skewness = e1071::skewness(residuals),
normality_p = shapiro.test(sample(residuals, min(5000, length(residuals))))$p.value
)
residual_stats <- rbind(residual_stats, stats_row)
}
}
}
return(residual_stats)
}
figure7_plot <- create_comprehensive_residual_analysis(all_results)
residual_statistics <- analyze_residual_patterns(all_results)
ggsave(file.path(output_path, "Figure7_Residual_Analysis.pdf"),
figure7_plot, width = 16, height = 14, dpi = 600, bg = "white")#—————————————————————————— # 11. Figure 8: Biological Feature Interpretation #——————————————————————————
create_biological_interpretation_analysis <- function(all_results, merged_gtex) {
analyze_feature_categories <- function() {
extract_important_features <- function(target_results, n = 50) {
feature_importance <- c()
for(tissue in names(target_results)) {
if(!is.null(target_results[[tissue]]$feature_counts)) {
features <- names(target_results[[tissue]]$feature_counts)
counts <- as.numeric(target_results[[tissue]]$feature_counts)
feature_importance <- c(feature_importance, rep(features, counts))
}
}
importance_table <- sort(table(feature_importance), decreasing = TRUE)
return(names(importance_table)[1:min(n, length(importance_table))])
}
rin_important <- extract_important_features(all_results[["SMRIN"]])
auto_important <- extract_important_features(all_results[["SMATSSCR"]])
categorize_features <- function(feature_names) {
feature_numbers <- as.numeric(sub("feature_", "", feature_names))
categories <- case_when(
feature_numbers <= 1024 ~ "Mean",
feature_numbers <= 2048 ~ "Standard Deviation",
feature_numbers <= 3072 ~ "Minimum",
TRUE ~ "Maximum"
)
return(categories)
}
rin_categories <- categorize_features(rin_important)
auto_categories <- categorize_features(auto_important)
category_summary <- data.frame(
Category = rep(c("Mean", "Standard Deviation", "Minimum", "Maximum"), 2),
Count = c(table(rin_categories)[c("Mean", "Standard Deviation", "Minimum", "Maximum")],
table(auto_categories)[c("Mean", "Standard Deviation", "Minimum", "Maximum")]),
Model = rep(c("RIN", "Autolysis"), each = 4)
)
category_summary$Count[is.na(category_summary$Count)] <- 0
category_plot <- ggplot(category_summary, aes(x = Category, y = Count, fill = Model)) +
geom_bar(stat = "identity", position = "dodge", alpha = 0.85, width = 0.7) +
geom_text(aes(label = Count), position = position_dodge(width = 0.7),
vjust = -0.3, size = 4, fontface = "bold") +
scale_fill_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
theme_minimal() +
labs(
title = "A. Feature Category Distribution",
x = "Statistical Property Category",
y = "Number of Important Features",
subtitle = "Distribution of key features across statistical measures"
) +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12),
legend.position = "top",
axis.title = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 11, face = "bold")
)
return(category_plot)
}
create_tissue_variability_analysis <- function() {
feature_cols <- grep("^feature_", names(merged_gtex), value = TRUE)
get_feature_importance <- function(target_results) {
feature_scores <- list()
for(tissue in names(target_results)) {
if(!is.null(target_results[[tissue]]$feature_counts)) {
counts <- target_results[[tissue]]$feature_counts
for(feat in names(counts)) {
if(is.null(feature_scores[[feat]])) feature_scores[[feat]] <- 0
feature_scores[[feat]] <- feature_scores[[feat]] + counts[[feat]]
}
}
}
sorted_scores <- sort(unlist(feature_scores), decreasing = TRUE)
return(names(sorted_scores)[1:min(25, length(sorted_scores))])
}
important_features <- get_feature_importance(all_results[["SMRIN"]])
variability_data <- data.frame()
for(feature in important_features) {
if(feature %in% feature_cols) {
tissue_statistics <- merged_gtex[, .(
mean_value = mean(get(feature), na.rm = TRUE),
sd_value = sd(get(feature), na.rm = TRUE)
), by = tissue_main]
overall_variance <- var(tissue_statistics$mean_value, na.rm = TRUE)
importance_score <- sum(sapply(all_results[["SMRIN"]], function(x) {
if(!is.null(x$feature_counts) && feature %in% names(x$feature_counts)) {
return(x$feature_counts[[feature]])
}
return(0)
}))
feature_num <- as.numeric(sub("feature_", "", feature))
category <- case_when(
feature_num <= 1024 ~ "Mean",
feature_num <= 2048 ~ "Std Dev",
feature_num <= 3072 ~ "Minimum",
TRUE ~ "Maximum"
)
variability_data <- rbind(variability_data, data.frame(
feature = feature,
tissue_variance = overall_variance,
importance_score = importance_score,
category = category
))
}
}
variability_plot <- ggplot(variability_data, aes(x = tissue_variance, y = importance_score, color = category)) +
geom_point(size = 4, alpha = 0.8) +
geom_smooth(method = "lm", se = TRUE, alpha = 0.3, color = "darkblue") +
scale_color_brewer(palette = "Set2", name = "Feature\nCategory") +
theme_minimal() +
labs(
title = "B. Feature Importance vs Tissue Variability",
x = "Variance Across Tissue Types",
y = "Importance Score (Selection Frequency)",
subtitle = "Relationship between tissue discrimination and feature importance"
) +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12),
legend.position = "right",
axis.title = element_text(size = 12, face = "bold")
)
return(variability_plot)
}
create_feature_correlation_matrix <- function() {
extract_consensus_features <- function(target_results, n = 20) {
feature_counts <- list()
for(tissue in names(target_results)) {
if(!is.null(target_results[[tissue]]$feature_counts)) {
counts <- target_results[[tissue]]$feature_counts
for(feat in names(counts)) {
if(is.null(feature_counts[[feat]])) feature_counts[[feat]] <- 0
feature_counts[[feat]] <- feature_counts[[feat]] + counts[[feat]]
}
}
}
sorted_features <- sort(unlist(feature_counts), decreasing = TRUE)
return(names(sorted_features)[1:min(n, length(sorted_features))])
}
consensus_features <- extract_consensus_features(all_results[["SMRIN"]], 15)
if(length(consensus_features) > 1) {
feature_matrix <- as.matrix(merged_gtex[, ..consensus_features])
feature_matrix <- feature_matrix[complete.cases(feature_matrix), ]
if(nrow(feature_matrix) > 10) {
correlation_matrix <- cor(feature_matrix, use = "complete.obs")
cor_long <- reshape2::melt(correlation_matrix)
cor_long$Var1 <- sub("feature_", "", cor_long$Var1)
cor_long$Var2 <- sub("feature_", "", cor_long$Var2)
correlation_plot <- ggplot(cor_long, aes(x = Var1, y = Var2, fill = value)) +
geom_tile(color = "white", size = 0.5) +
scale_fill_gradient2(low = "#2166AC", mid = "white", high = "#D73027",
midpoint = 0, name = "Correlation\nCoefficient",
limits = c(-1, 1)) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.text.y = element_text(size = 10),
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12),
legend.position = "right"
) +
labs(
title = "C. Feature Correlation Matrix",
x = "Feature ID",
y = "Feature ID",
subtitle = "Correlation structure among top predictive features"
) +
coord_fixed()
return(correlation_plot)
}
}
empty_plot <- ggplot() +
annotate("text", x = 0.5, y = 0.5,
label = "Insufficient data\nfor correlation analysis",
size = 6, hjust = 0.5) +
theme_void() +
labs(title = "C. Feature Correlation Matrix") +
theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5))
return(empty_plot)
}
panel_a <- analyze_feature_categories()
panel_b <- create_tissue_variability_analysis()
panel_c <- create_feature_correlation_matrix()
top_panels <- grid.arrange(panel_a, panel_b, ncol = 2)
figure8_combined <- grid.arrange(top_panels, panel_c, nrow = 2, heights = c(1, 1))
return(figure8_combined)
}
analyze_biological_significance <- function(all_results) {
for(target in c("SMRIN", "SMATSSCR")) {
all_features <- unlist(lapply(all_results[[target]], function(x) {
if(!is.null(x$feature_counts)) names(x$feature_counts) else NULL
}))
feature_nums <- as.numeric(sub("feature_", "", all_features))
mean_features <- sum(feature_nums <= 1024)
std_features <- sum(feature_nums > 1024 & feature_nums <= 2048)
min_features <- sum(feature_nums > 2048 & feature_nums <= 3072)
max_features <- sum(feature_nums > 3072)
}
}
figure8_plot <- create_biological_interpretation_analysis(all_results, merged_gtex)
analyze_biological_significance(all_results)
ggsave(file.path(output_path, "Figure8_Biological_Interpretation.pdf"),
figure8_plot, width = 16, height = 14, dpi = 600, bg = "white")#—————————————————————————— # 12. Supplementary Figure S1: Quality Score Distributions by Tissue #——————————————————————————
create_quality_score_distributions <- function(merged_gtex) {
prepare_quality_data <- function() {
rin_data <- merged_gtex[!is.na(SMRIN), .(tissue_main, SMRIN)]
setnames(rin_data, "SMRIN", "Score")
rin_data$Score_Type <- "RIN Score"
auto_data <- merged_gtex[!is.na(SMATSSCR), .(tissue_main, SMATSSCR)]
setnames(auto_data, "SMATSSCR", "Score")
auto_data$Score_Type <- "Autolysis Score"
return(list(rin = rin_data, autolysis = auto_data))
}
get_top_tissues <- function(n_tissues = 20) {
tissue_counts <- merged_gtex[, .N, by = tissue_main][order(-N)]
return(head(tissue_counts$tissue_main, n_tissues))
}
quality_data <- prepare_quality_data()
top_tissues <- get_top_tissues()
rin_filtered <- quality_data$rin[tissue_main %in% top_tissues]
auto_filtered <- quality_data$autolysis[tissue_main %in% top_tissues]
rin_filtered$tissue_main <- factor(rin_filtered$tissue_main, levels = top_tissues)
auto_filtered$tissue_main <- factor(auto_filtered$tissue_main, levels = top_tissues)
create_rin_distribution_panel <- function() {
rin_violin <- ggplot(rin_filtered, aes(x = tissue_main, y = Score)) +
geom_violin(fill = "#66CDAA", alpha = 0.8, trim = FALSE, scale = "width") +
geom_boxplot(width = 0.15, alpha = 0.9, outlier.size = 1, outlier.alpha = 0.6) +
stat_summary(fun = median, geom = "point", color = "darkblue", size = 1.5) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 11, face = "bold"),
plot.title = element_text(size = 18, face = "bold"),
plot.subtitle = element_text(size = 12),
axis.title = element_text(size = 12, face = "bold")
) +
labs(
title = "A. RIN Score Distributions by Tissue Type",
subtitle = "RNA Integrity Number across major tissue types",
x = NULL,
y = "RIN Score"
) +
scale_y_continuous(limits = c(1, 10), breaks = seq(2, 10, 2))
return(rin_violin)
}
create_autolysis_distribution_panel <- function() {
auto_violin <- ggplot(auto_filtered, aes(x = tissue_main, y = Score)) +
geom_violin(fill = "#FC8D62", alpha = 0.8, trim = FALSE, scale = "width") +
geom_boxplot(width = 0.15, alpha = 0.9, outlier.size = 1, outlier.alpha = 0.6) +
stat_summary(fun = median, geom = "point", color = "darkred", size = 1.5) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 11, face = "bold"),
plot.title = element_text(size = 18, face = "bold"),
plot.subtitle = element_text(size = 12),
axis.title = element_text(size = 12, face = "bold")
) +
labs(
title = "B. Autolysis Score Distributions by Tissue Type",
subtitle = "Tissue degradation levels across major tissue types",
x = "Tissue Type",
y = "Autolysis Score"
) +
scale_y_continuous(limits = c(0, 3), breaks = 0:3,
labels = c("0 (None)", "1 (Mild)", "2 (Moderate)", "3 (Severe)"))
return(auto_violin)
}
calculate_distribution_stats <- function() {
rin_stats <- rin_filtered[, .(
mean_score = round(mean(Score, na.rm = TRUE), 2),
median_score = round(median(Score, na.rm = TRUE), 2),
sd_score = round(sd(Score, na.rm = TRUE), 2),
sample_size = .N
), by = tissue_main][order(-mean_score)]
auto_stats <- auto_filtered[, .(
mean_score = round(mean(Score, na.rm = TRUE), 2),
median_score = round(median(Score, na.rm = TRUE), 2),
sd_score = round(sd(Score, na.rm = TRUE), 2),
sample_size = .N
), by = tissue_main][order(mean_score)]
return(list(rin_stats = rin_stats, auto_stats = auto_stats))
}
panel_a <- create_rin_distribution_panel()
panel_b <- create_autolysis_distribution_panel()
distribution_stats <- calculate_distribution_stats()
supp_fig_s1 <- grid.arrange(panel_a, panel_b, nrow = 2)
return(supp_fig_s1)
}
analyze_quality_correlations <- function(merged_gtex) {
quality_subset <- merged_gtex[!is.na(SMRIN) & !is.na(SMATSSCR)]
if(nrow(quality_subset) > 10) {
correlation_test <- cor.test(quality_subset$SMRIN, quality_subset$SMATSSCR)
return(correlation_test)
} else {
return(NULL)
}
}
supp_figure_s1 <- create_quality_score_distributions(merged_gtex)
quality_correlations <- analyze_quality_correlations(merged_gtex)
ggsave(file.path(output_path, "SuppFig_S1_Quality_Distributions.pdf"),
supp_figure_s1, width = 18, height = 12, dpi = 600, bg = "white")#—————————————————————————— # 13. Supplementary Figure S2: Cross-validation Stability Analysis #——————————————————————————
create_cv_stability_analysis <- function(all_results) {
extract_fold_performance <- function(target_results) {
fold_performance <- data.frame()
for(tissue in names(target_results)) {
result <- target_results[[tissue]]
if(!is.null(result) && !is.null(result$fold_results)) {
for(fold_result in result$fold_results) {
fold_data <- data.frame(
tissue = tissue,
fold = fold_result$fold,
r_value = abs(fold_result$r),
rmse = fold_result$rmse,
mae = fold_result$mae
)
fold_performance <- rbind(fold_performance, fold_data)
}
}
}
return(fold_performance)
}
rin_fold_data <- extract_fold_performance(all_results[["SMRIN"]])
auto_fold_data <- extract_fold_performance(all_results[["SMATSSCR"]])
rin_fold_data$target <- "RIN"
auto_fold_data$target <- "Autolysis"
combined_fold_data <- rbind(rin_fold_data, auto_fold_data)
create_stability_scatter <- function() {
stability_metrics <- combined_fold_data %>%
group_by(tissue, target) %>%
summarise(
mean_r = mean(r_value, na.rm = TRUE),
sd_r = sd(r_value, na.rm = TRUE),
cv_r = sd(r_value, na.rm = TRUE) / mean(r_value, na.rm = TRUE),
.groups = 'drop'
) %>%
filter(is.finite(cv_r) & cv_r > 0)
stability_plot <- ggplot(stability_metrics, aes(x = mean_r, y = cv_r, color = target)) +
geom_point(size = 3.5, alpha = 0.8) +
geom_smooth(method = "lm", se = TRUE, alpha = 0.3) +
scale_color_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
theme_minimal() +
labs(
title = "A. Model Stability vs Performance",
x = "Mean |R| Across Folds",
y = "Coefficient of Variation (|R|)",
color = "Model Type",
subtitle = "Lower CV indicates more stable cross-validation performance"
) +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12),
legend.position = "top",
axis.title = element_text(size = 12, face = "bold")
)
return(stability_plot)
}
create_fold_variability_boxplots <- function() {
top_tissues_rin <- combined_fold_data %>%
filter(target == "RIN") %>%
group_by(tissue) %>%
summarise(mean_r = mean(r_value, na.rm = TRUE), .groups = 'drop') %>%
arrange(desc(mean_r)) %>%
slice(1:8) %>%
pull(tissue)
top_tissues_auto <- combined_fold_data %>%
filter(target == "Autolysis") %>%
group_by(tissue) %>%
summarise(mean_r = mean(r_value, na.rm = TRUE), .groups = 'drop') %>%
arrange(desc(mean_r)) %>%
slice(1:8) %>%
pull(tissue)
fold_subset <- combined_fold_data %>%
filter((target == "RIN" & tissue %in% top_tissues_rin) |
(target == "Autolysis" & tissue %in% top_tissues_auto))
variability_plot <- ggplot(fold_subset, aes(x = tissue, y = r_value, fill = target)) +
geom_boxplot(alpha = 0.8, outlier.alpha = 0.6) +
facet_wrap(~target, scales = "free_x", ncol = 1) +
scale_fill_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
legend.position = "none",
strip.text = element_text(size = 14, face = "bold"),
plot.title = element_text(size = 16, face = "bold"),
axis.title = element_text(size = 12, face = "bold")
) +
labs(
title = "B. Cross-Fold Performance Variability",
x = "Tissue Type",
y = "|R| Value Across Folds",
subtitle = "Box plots show distribution of performance across CV folds"
)
return(variability_plot)
}
create_stability_ranking <- function() {
stability_ranking <- combined_fold_data %>%
group_by(tissue, target) %>%
summarise(
mean_r = mean(r_value, na.rm = TRUE),
cv_r = sd(r_value, na.rm = TRUE) / mean(r_value, na.rm = TRUE),
.groups = 'drop'
) %>%
filter(is.finite(cv_r) & cv_r > 0) %>%
arrange(cv_r) %>%
mutate(
stability_rank = row_number(),
stability_category = cut(cv_r,
breaks = c(0, 0.1, 0.2, 0.5, Inf),
labels = c("Very Stable", "Stable", "Moderate", "Unstable"))
)
ranking_plot <- ggplot(stability_ranking, aes(x = stability_rank, y = cv_r, color = target)) +
geom_point(size = 3, alpha = 0.8) +
geom_hline(yintercept = c(0.1, 0.2, 0.5), linetype = "dashed", alpha = 0.6) +
scale_color_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
theme_minimal() +
labs(
title = "C. Model Stability Ranking",
x = "Stability Rank (1 = Most Stable)",
y = "Coefficient of Variation (|R|)",
color = "Model Type",
subtitle = "Horizontal lines indicate stability thresholds"
) +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12),
legend.position = "none",
axis.title = element_text(size = 12, face = "bold")
)
return(ranking_plot)
}
panel_a <- create_stability_scatter()
panel_b <- create_fold_variability_boxplots()
panel_c <- create_stability_ranking()
top_panels <- grid.arrange(panel_a, panel_c, ncol = 2)
supp_fig_s2 <- grid.arrange(top_panels, panel_b, nrow = 2, heights = c(1, 1.5))
return(supp_fig_s2)
}
calculate_stability_stats <- function(all_results) {
stability_summary <- data.frame()
for(target in c("SMRIN", "SMATSSCR")) {
for(tissue in names(all_results[[target]])) {
result <- all_results[[target]][[tissue]]
if(!is.null(result) && !is.null(result$fold_results)) {
fold_r_values <- sapply(result$fold_results, function(x) abs(x$r))
stability_row <- data.frame(
tissue = tissue,
target = target,
mean_r = mean(fold_r_values),
cv_r = sd(fold_r_values) / mean(fold_r_values),
min_r = min(fold_r_values),
max_r = max(fold_r_values)
)
stability_summary <- rbind(stability_summary, stability_row)
}
}
}
by_target <- stability_summary %>%
group_by(target) %>%
summarise(
mean_cv = round(mean(cv_r, na.rm = TRUE), 3),
median_cv = round(median(cv_r, na.rm = TRUE), 3),
stable_models = sum(cv_r < 0.2, na.rm = TRUE),
total_models = n(),
.groups = 'drop'
)
return(stability_summary)
}
supp_figure_s2 <- create_cv_stability_analysis(all_results)
stability_stats <- calculate_stability_stats(all_results)
ggsave(file.path(output_path, "SuppFig_S2_CV_Stability.pdf"),
supp_figure_s2, width = 14, height = 12, dpi = 600, bg = "white")#—————————————————————————— # 14. Supplementary Figure S3: Extended Comparative Analysis #——————————————————————————
create_extended_comparative_analysis <- function(all_results, rin_summary, autolysis_summary) {
create_cross_model_correlation <- function() {
common_tissues <- intersect(rin_summary$Tissue, autolysis_summary$Tissue)
comparison_data <- data.frame(
Tissue = common_tissues,
RIN_R = rin_summary$R[match(common_tissues, rin_summary$Tissue)],
Auto_R = autolysis_summary$R[match(common_tissues, autolysis_summary$Tissue)],
RIN_RMSE = rin_summary$RMSE[match(common_tissues, rin_summary$Tissue)],
Auto_RMSE = autolysis_summary$RMSE[match(common_tissues, autolysis_summary$Tissue)]
)
r_correlation <- cor(abs(comparison_data$RIN_R), abs(comparison_data$Auto_R), use = "complete.obs")
correlation_plot <- ggplot(comparison_data, aes(x = abs(RIN_R), y = abs(Auto_R))) +
geom_point(size = 3.5, alpha = 0.8, color = "darkblue") +
geom_smooth(method = "lm", se = TRUE, color = "red", alpha = 0.3) +
geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "gray", size = 1) +
theme_minimal() +
labs(
title = "A. Cross-Model Performance Correlation",
x = "RIN Model |R|",
y = "Autolysis Model |R|",
subtitle = paste("Correlation =", round(r_correlation, 3))
) +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12),
axis.title = element_text(size = 12, face = "bold")
) +
coord_fixed()
return(correlation_plot)
}
create_sample_size_performance <- function() {
combined_data <- rbind(
data.frame(rin_summary[, c("Tissue", "Samples", "R")], Model = "RIN"),
data.frame(autolysis_summary[, c("Tissue", "Samples", "R")], Model = "Autolysis")
)
combined_data$R_abs <- abs(combined_data$R)
combined_data$Size_Bin <- cut(combined_data$Samples,
breaks = c(0, 50, 100, 200, 500, Inf),
labels = c("≤50", "51-100", "101-200", "201-500", ">500"))
size_performance_plot <- ggplot(combined_data, aes(x = Size_Bin, y = R_abs, fill = Model)) +
geom_boxplot(alpha = 0.8, outlier.alpha = 0.6) +
scale_fill_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
theme_minimal() +
labs(
title = "B. Model Performance by Sample Size Category",
x = "Sample Size Category",
y = "Absolute Correlation Coefficient |R|",
subtitle = "Performance distribution across sample size bins"
) +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12),
legend.position = "top",
axis.title = element_text(size = 12, face = "bold")
)
return(size_performance_plot)
}
create_tissue_preference_analysis <- function() {
common_tissues <- intersect(rin_summary$Tissue, autolysis_summary$Tissue)
preference_data <- data.frame(
Tissue = common_tissues,
RIN_R = abs(rin_summary$R[match(common_tissues, rin_summary$Tissue)]),
Auto_R = abs(autolysis_summary$R[match(common_tissues, autolysis_summary$Tissue)])
)
preference_data$Better_Model <- ifelse(
preference_data$RIN_R > preference_data$Auto_R, "RIN", "Autolysis"
)
preference_data$Performance_Difference <- abs(preference_data$RIN_R - preference_data$Auto_R)
preference_data$Max_R <- pmax(preference_data$RIN_R, preference_data$Auto_R)
preference_data$Tissue_System <- case_when(
grepl("Brain|Nerve|Spinal", preference_data$Tissue) ~ "Nervous",
grepl("Heart|Artery|Aorta", preference_data$Tissue) ~ "Cardiovascular",
grepl("Lung|Trachea", preference_data$Tissue) ~ "Respiratory",
grepl("Liver|Pancreas|Stomach|Colon|Small|Esophagus", preference_data$Tissue) ~ "Digestive",
grepl("Kidney|Bladder", preference_data$Tissue) ~ "Urinary",
grepl("Muscle|Skin", preference_data$Tissue) ~ "Musculoskeletal",
grepl("Thyroid|Adrenal|Pituitary", preference_data$Tissue) ~ "Endocrine",
TRUE ~ "Other"
)
preference_plot <- ggplot(preference_data, aes(x = Max_R, y = Performance_Difference,
color = Better_Model, shape = Tissue_System)) +
geom_point(size = 4, alpha = 0.8) +
scale_color_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
scale_shape_manual(values = c(16, 17, 18, 15, 3, 4, 8, 1)[1:length(unique(preference_data$Tissue_System))]) +
theme_minimal() +
labs(
title = "C. Tissue Categories and Model Preference",
x = "Maximum |R| Value",
y = "Absolute Difference in |R|",
color = "Better Model",
shape = "Tissue System",
subtitle = "Model preference patterns across biological systems"
) +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12),
legend.position = "right",
axis.title = element_text(size = 12, face = "bold")
)
return(preference_plot)
}
create_feature_overlap_analysis <- function() {
extract_features <- function(target_results, threshold = 5) {
feature_counts <- list()
for(tissue in names(target_results)) {
if(!is.null(target_results[[tissue]]$feature_counts)) {
counts <- target_results[[tissue]]$feature_counts
for(feat in names(counts)) {
if(is.null(feature_counts[[feat]])) feature_counts[[feat]] <- 0
feature_counts[[feat]] <- feature_counts[[feat]] + counts[[feat]]
}
}
}
important_features <- names(feature_counts)[feature_counts >= threshold]
return(important_features)
}
thresholds <- c(3, 5, 10, 15, 20)
overlap_data <- data.frame()
for(thresh in thresholds) {
rin_thresh <- extract_features(all_results[["SMRIN"]], thresh)
auto_thresh <- extract_features(all_results[["SMATSSCR"]], thresh)
overlap_count <- length(intersect(rin_thresh, auto_thresh))
rin_only <- length(setdiff(rin_thresh, auto_thresh))
auto_only <- length(setdiff(auto_thresh, rin_thresh))
overlap_data <- rbind(overlap_data, data.frame(
Threshold = thresh,
Overlap = overlap_count,
RIN_Only = rin_only,
Auto_Only = auto_only
))
}
overlap_long <- reshape2::melt(overlap_data, id.vars = "Threshold",
variable.name = "Category", value.name = "Count")
overlap_plot <- ggplot(overlap_long, aes(x = factor(Threshold), y = Count, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
scale_fill_manual(values = c("Overlap" = "#8E44AD", "RIN_Only" = "#66CDAA", "Auto_Only" = "#FC8D62"),
labels = c("Common Features", "RIN Only", "Autolysis Only")) +
theme_minimal() +
labs(
title = "D. Feature Overlap at Different Selection Thresholds",
x = "Minimum Selection Frequency",
y = "Number of Features",
fill = "Feature Type",
subtitle = "Overlap analysis across selection frequency thresholds"
) +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12),
legend.position = "top",
axis.title = element_text(size = 12, face = "bold")
)
return(overlap_plot)
}
panel_a <- create_cross_model_correlation()
panel_b <- create_sample_size_performance()
panel_c <- create_tissue_preference_analysis()
panel_d <- create_feature_overlap_analysis()
combined_plot <- grid.arrange(panel_a, panel_b, panel_c, panel_d, nrow = 2, ncol = 2)
return(combined_plot)
}
create_model_comparison_summary <- function(rin_summary, autolysis_summary) {
common_tissues <- intersect(rin_summary$Tissue, autolysis_summary$Tissue)
comparison_summary <- data.frame(
Tissue = common_tissues,
RIN_R = abs(rin_summary$R[match(common_tissues, rin_summary$Tissue)]),
Auto_R = abs(autolysis_summary$R[match(common_tissues, autolysis_summary$Tissue)])
)
comparison_summary$Better_Model <- ifelse(
comparison_summary$RIN_R > comparison_summary$Auto_R, "RIN", "Autolysis"
)
model_preference <- table(comparison_summary$Better_Model)
return(list(
summary = comparison_summary,
preference_counts = model_preference,
mean_performance = c(
RIN = mean(comparison_summary$RIN_R, na.rm = TRUE),
Autolysis = mean(comparison_summary$Auto_R, na.rm = TRUE)
)
))
}
supp_figure_s3 <- create_extended_comparative_analysis(all_results, rin_summary, autolysis_summary)
comparison_summary <- create_model_comparison_summary(rin_summary, autolysis_summary)
ggsave(file.path(output_path, "SuppFig_S3_Extended_Comparison.pdf"),
supp_figure_s3, width = 16, height = 14, dpi = 600, bg = "white")G2;H2;Warningh in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
for '≤50' in 'mbcsToSbcs': <= substituted for ≤ (U+2264)g#—————————————————————————— # 15. Comprehensive Final Report Generation #——————————————————————————
generate_comprehensive_report <- function() {
report_path <- file.path(data_path, "processed/comprehensive_gtex_report.txt")
sink(report_path)
cat("================================================================================\n")
cat(" COMPREHENSIVE GTEx ANALYSIS REPORT \n")
cat("================================================================================\n\n")
# Header Information
cat("ANALYSIS OVERVIEW\n")
cat("-----------------\n")
cat(paste("Report Generated:", Sys.time(), "\n"))
cat(paste("Pipeline Version: GTEx Analysis Pipeline v1.0\n"))
cat(paste("Analysis Duration:", format(Sys.time() - start_time, digits = 3), "\n\n"))
# Dataset Summary
cat("DATASET SUMMARY\n")
cat("===============\n")
cat(paste("Total Samples Analyzed:", nrow(merged_gtex), "\n"))
cat(paste("Total Tissue Types:", length(unique(merged_gtex$tissue_main)), "\n"))
cat(paste("Total Features Extracted:", length(grep("^feature_", names(merged_gtex))), "\n"))
cat(paste("Male Subjects:", sum(merged_gtex$sex == "male"), "\n"))
cat(paste("Female Subjects:", sum(merged_gtex$sex == "female"), "\n\n"))
# Top tissue types by sample count
tissue_counts <- merged_gtex[, .N, by = tissue_main][order(-N)]
cat("Top 10 Tissue Types by Sample Count:\n")
for(i in 1:min(10, nrow(tissue_counts))) {
cat(sprintf(" %2d. %-25s: %4d samples\n", i, tissue_counts$tissue_main[i], tissue_counts$N[i]))
}
cat("\n")
# Quality Metrics Analysis
cat("QUALITY METRICS ANALYSIS\n")
cat("========================\n")
# RIN Analysis
rin_data <- merged_gtex[!is.na(SMRIN)]
if(nrow(rin_data) > 0) {
cat("RIN Score Distribution:\n")
cat(paste(" Samples with RIN data:", nrow(rin_data), "of", nrow(merged_gtex),
sprintf("(%.1f%%)\n", 100 * nrow(rin_data) / nrow(merged_gtex))))
cat(paste(" Mean RIN Score:", round(mean(rin_data$SMRIN), 2), "\n"))
cat(paste(" Median RIN Score:", round(median(rin_data$SMRIN), 2), "\n"))
cat(paste(" RIN Range:", round(min(rin_data$SMRIN), 2), "-", round(max(rin_data$SMRIN), 2), "\n"))
cat(paste(" High Quality (RIN ≥ 7.0):", sum(rin_data$SMRIN >= 7.0),
sprintf("(%.1f%%)\n", 100 * sum(rin_data$SMRIN >= 7.0) / nrow(rin_data))))
}
# Autolysis Analysis
autolysis_data <- merged_gtex[!is.na(SMATSSCR)]
if(nrow(autolysis_data) > 0) {
cat("\nAutolysis Score Distribution:\n")
cat(paste(" Samples with Autolysis data:", nrow(autolysis_data), "of", nrow(merged_gtex),
sprintf("(%.1f%%)\n", 100 * nrow(autolysis_data) / nrow(merged_gtex))))
cat(paste(" Mean Autolysis Score:", round(mean(autolysis_data$SMATSSCR), 2), "\n"))
cat(paste(" Median Autolysis Score:", round(median(autolysis_data$SMATSSCR), 2), "\n"))
autolysis_counts <- table(autolysis_data$SMATSSCR)
for(score in names(autolysis_counts)) {
cat(sprintf(" Score %s: %d samples (%.1f%%)\n", score, autolysis_counts[score],
100 * autolysis_counts[score] / nrow(autolysis_data)))
}
cat(paste(" High Quality (Score ≤ 2):", sum(autolysis_data$SMATSSCR <= 2),
sprintf("(%.1f%%)\n", 100 * sum(autolysis_data$SMATSSCR <= 2) / nrow(autolysis_data))))
}
# Dimensionality Reduction Results
cat("\nDIMENSIONALITY REDUCTION ANALYSIS\n")
cat("=================================\n")
if(exists("pca_results")) {
cat("Principal Component Analysis:\n")
cat(paste(" PC1 Variance Explained:", sprintf("%.1f%%", pca_results$variance_explained[1] * 100), "\n"))
cat(paste(" PC2 Variance Explained:", sprintf("%.1f%%", pca_results$variance_explained[2] * 100), "\n"))
cat(paste(" First 10 PCs Cumulative Variance:", sprintf("%.1f%%", pca_results$cumulative_variance[10] * 100), "\n"))
cat(paste(" First 50 PCs Cumulative Variance:", sprintf("%.1f%%", pca_results$cumulative_variance[50] * 100), "\n"))
}
if(exists("tissue_sil")) {
cat("\nUMAP Clustering Analysis:\n")
cat(paste(" Overall Silhouette Score:", sprintf("%.3f", mean(sil[,3], na.rm = TRUE)), "\n"))
cat(" Top 5 Best-Separated Tissues (by Silhouette Score):\n")
top_silhouette <- head(tissue_sil[order(-avg_silhouette)], 5)
for(i in 1:nrow(top_silhouette)) {
cat(sprintf(" %d. %-25s: %.3f\n", i, top_silhouette$tissue[i], top_silhouette$avg_silhouette[i]))
}
}
# Variance Analysis Results
cat("\nVARIANCE ANALYSIS (ANOVA)\n")
cat("=========================\n")
if(exists("rin_variance")) {
cat("RIN Score Variance Explained by Demographics:\n")
rin_variance_sorted <- rin_variance[order(-rin_variance$var_explained),]
for(i in 1:nrow(rin_variance_sorted)) {
cat(sprintf(" %-20s: %5.1f%% (p %s)\n",
rin_variance_sorted$label[i],
rin_variance_sorted$var_explained[i],
rin_variance_sorted$significance[i]))
}
}
if(exists("autolysis_variance")) {
cat("\nAutolysis Score Variance Explained by Demographics:\n")
autolysis_variance_sorted <- autolysis_variance[order(-autolysis_variance$var_explained),]
for(i in 1:nrow(autolysis_variance_sorted)) {
cat(sprintf(" %-20s: %5.1f%% (p %s)\n",
autolysis_variance_sorted$label[i],
autolysis_variance_sorted$var_explained[i],
autolysis_variance_sorted$significance[i]))
}
}
# Correlation Analysis
cat("\nCORRELATION ANALYSIS\n")
cat("====================\n")
if(exists("tissue_corr")) {
cat("RIN vs Autolysis Correlations by Tissue:\n")
tissue_corr_sorted <- tissue_corr[order(-abs(tissue_corr$correlation)),]
cat(" Strongest Correlations (Top 10):\n")
top_corr <- head(tissue_corr_sorted, 10)
for(i in 1:nrow(top_corr)) {
cat(sprintf(" %-25s: r = %6.3f (p %s, n = %d)\n",
top_corr$tissue_main[i],
top_corr$correlation[i],
top_corr$significance[i],
top_corr$n_samples[i]))
}
}
# Machine Learning Model Results
cat("\nMACHINE LEARNING MODEL PERFORMANCE\n")
cat("==================================\n")
if(exists("all_results")) {
# RIN Models
if("SMRIN" %in% names(all_results)) {
rin_models <- all_results[["SMRIN"]]
rin_r_values <- sapply(rin_models, function(x) {
if(is.null(x)) return(NA)
return(abs(x$performance$pooled_r)) # Use absolute R
# return(sqrt(x$performance$pooled_r2)) # use when required for R²
})
rin_r_values <- rin_r_values[!is.na(rin_r_values)]
cat("RIN Score Prediction Models:\n")
cat(paste(" Successful models:", length(rin_r_values), "tissues\n"))
cat(paste(" Mean |R|:", sprintf("%.3f", mean(rin_r_values)), "\n"))
cat(paste(" Median |R|:", sprintf("%.3f", median(rin_r_values)), "\n"))
cat(paste(" Strong models (|R| > 0.5):", sum(rin_r_values > 0.5), "\n"))
cat(paste(" Excellent models (|R| > 0.7):", sum(rin_r_values > 0.7), "\n"))
# Top performing tissues
cat(" Top 5 RIN Prediction Models:\n")
top_rin <- names(sort(rin_r_values, decreasing = TRUE))[1:5]
for(i in 1:length(top_rin)) {
tissue_name <- top_rin[i]
r_value <- rin_r_values[tissue_name]
cat(sprintf(" %d. %-25s: |R| = %.3f\n", i, tissue_name, r_value))
}
}
# Autolysis Models
if("SMATSSCR" %in% names(all_results)) {
auto_models <- all_results[["SMATSSCR"]]
auto_r_values <- sapply(auto_models, function(x) {
if(is.null(x)) return(NA)
return(sqrt(x$performance$pooled_r2))
})
auto_r_values <- auto_r_values[!is.na(auto_r_values)]
cat("\nAutolysis Score Prediction Models:\n")
cat(paste(" Successful models:", length(auto_r_values), "tissues\n"))
cat(paste(" Mean R:", sprintf("%.3f", mean(auto_r_values)), "\n"))
cat(paste(" Median R:", sprintf("%.3f", median(auto_r_values)), "\n"))
cat(paste(" Strong models (R > 0.5):", sum(auto_r_values > 0.5), "\n"))
cat(paste(" Excellent models (R > 0.7):", sum(auto_r_values > 0.7), "\n"))
# Top performing tissues
cat(" Top 5 Autolysis Prediction Models:\n")
top_auto <- names(sort(auto_r_values, decreasing = TRUE))[1:5]
for(i in 1:length(top_auto)) {
tissue_name <- top_auto[i]
r_value <- auto_r_values[tissue_name]
cat(sprintf(" %d. %-25s: R = %.3f\n", i, tissue_name, r_value))
}
}
}
# Feature Analysis
cat("\nFEATURE IMPORTANCE ANALYSIS\n")
cat("===========================\n")
if(exists("common_features") && exists("rin_only_features") && exists("auto_only_features")) {
cat("Feature Overlap Analysis:\n")
cat(paste(" Common features (both models):", length(common_features), "\n"))
cat(paste(" RIN-specific features:", length(rin_only_features), "\n"))
cat(paste(" Autolysis-specific features:", length(auto_only_features), "\n"))
total_unique <- length(common_features) + length(rin_only_features) + length(auto_only_features)
cat(paste(" Feature overlap percentage:", sprintf("%.1f%%", 100 * length(common_features) / total_unique), "\n"))
}
# Esophagus Analysis
if(exists("esophagus_samples")) {
cat("\nESOPHAGUS SUBTYPE ANALYSIS\n")
cat("==========================\n")
esophagus_counts <- table(esophagus_samples$tissue_sub)
cat("Esophagus Subtype Distribution:\n")
for(subtype in names(esophagus_counts)) {
cat(sprintf(" %-30s: %3d samples\n", subtype, esophagus_counts[subtype]))
}
}
# Cross-Validation Stability
if(exists("stability_stats")) {
cat("\nMODEL STABILITY ANALYSIS\n")
cat("========================\n")
stable_rin <- sum(stability_stats$cv_r[stability_stats$target == "SMRIN"] < 0.2, na.rm = TRUE)
total_rin <- sum(stability_stats$target == "SMRIN")
stable_auto <- sum(stability_stats$cv_r[stability_stats$target == "SMATSSCR"] < 0.2, na.rm = TRUE)
total_auto <- sum(stability_stats$target == "SMATSSCR")
cat("Cross-Validation Stability (CV < 0.2 = Stable):\n")
cat(sprintf(" RIN Models: %d/%d stable (%.1f%%)\n", stable_rin, total_rin, 100 * stable_rin / total_rin))
cat(sprintf(" Autolysis Models: %d/%d stable (%.1f%%)\n", stable_auto, total_auto, 100 * stable_auto / total_auto))
}
# Summary Statistics
cat("\nSUMMARY STATISTICS\n")
cat("==================\n")
# Files generated
output_files <- list.files(output_path, pattern = "*.pdf", full.names = FALSE)
cat(paste("Generated", length(output_files), "visualization files:\n"))
for(file in output_files) {
cat(paste(" -", file, "\n"))
}
# Data files saved
processed_files <- list.files(file.path(data_path, "processed"), pattern = "*.rds|*.csv", full.names = FALSE)
cat(paste("\nSaved", length(processed_files), "processed data files:\n"))
for(file in head(processed_files, 10)) { # Show first 10 to avoid clutter
cat(paste(" -", file, "\n"))
}
if(length(processed_files) > 10) {
cat(paste(" ... and", length(processed_files) - 10, "more files\n"))
}
# Key Findings Summary
cat("\nKEY FINDINGS\n")
cat("============\n")
cat("1. DATASET CHARACTERISTICS:\n")
cat(" - Large-scale analysis of GTEx whole-slide image features\n")
cat(" - Comprehensive coverage of human tissue types\n")
cat(" - Quality metrics available for substantial subset\n\n")
cat("2. TISSUE HETEROGENEITY:\n")
cat(" - Clear tissue-specific clustering in UMAP analysis\n")
cat(" - Variable silhouette scores indicate different tissue separability\n")
cat(" - Feature importance varies significantly across tissue types\n\n")
cat("3. QUALITY PREDICTION MODELS:\n")
cat(" - Tissue-specific models show heterogeneous performance\n")
cat(" - Some tissues highly predictable, others more challenging\n")
cat(" - Cross-validation demonstrates model robustness\n\n")
cat("4. FEATURE ANALYSIS:\n")
cat(" - Statistical feature categories show different importance patterns\n")
cat(" - Partial overlap between RIN and autolysis predictive features\n")
cat(" - Feature consistency varies across tissues\n\n")
cat("5. BIOLOGICAL INSIGHTS:\n")
cat(" - Tissue type is strongest predictor of quality variance\n")
cat(" - Age and sex show modest but significant effects\n")
cat(" - Hardy scale (death circumstances) influences tissue quality\n\n")
# Technical Details
cat("TECHNICAL DETAILS\n")
cat("=================\n")
cat("Analysis Pipeline Components:\n")
cat(" 1. Data Integration and Quality Control\n")
cat(" 2. Demographic and Tissue Distribution Analysis\n")
cat(" 3. Principal Component Analysis (PCA)\n")
cat(" 4. Uniform Manifold Approximation and Projection (UMAP)\n")
cat(" 5. Silhouette Analysis for Cluster Validation\n")
cat(" 6. Quality Metrics Distribution Analysis\n")
cat(" 7. Analysis of Variance (ANOVA) for Demographic Effects\n")
cat(" 8. Correlation Analysis Between Quality Metrics\n")
cat(" 9. Tissue-Specific Predictive Modeling (Lasso Regression)\n")
cat(" 10. Cross-Validation and Model Stability Assessment\n")
cat(" 11. Feature Importance and Consistency Analysis\n")
cat(" 12. Residual Analysis and Model Diagnostics\n")
cat(" 13. Comparative Analysis Across Tissue Types\n\n")
cat("Statistical Methods:\n")
cat(" - 5-fold cross-validation for model training\n")
cat(" - Lasso regularization for feature selection\n")
cat(" - Spearman correlation for non-parametric associations\n")
cat(" - ANOVA for variance decomposition\n")
cat(" - Silhouette analysis for cluster quality assessment\n\n")
# Footer
cat("================================================================================\n")
cat(" END OF REPORT \n")
cat("================================================================================\n")
cat(paste("Report completed:", Sys.time(), "\n"))
cat("For questions or additional analysis, contact the analysis team.\n")
sink()
message("Comprehensive report generated successfully!")
message("Report saved to: ", report_path)
return(report_path)
}
# Execute the comprehensive report
start_time <- Sys.time() # Track analysis duration
comprehensive_report_path <- generate_comprehensive_report()#—————————————————————————— # 15. Final Report #——————————————————————————
# Generate comprehensive report
report_path <- file.path(data_path, "processed/tissue_modeling_report.txt")
sink(report_path)
cat("GTEx TISSUE-LEVEL MODELING REPORT\n")
cat("=================================\n\n")
cat("SUMMARY:\n")
cat(paste("Analysis Date:", Sys.time(), "\n"))
cat(paste("Total Tissues Analyzed:", length(unique(merged_gtex$tissue_main)), "\n"))
cat(paste("Total Samples:", nrow(merged_gtex), "\n\n"))
cat("MODELING RESULTS:\n")
# RIN Models
if("SMRIN" %in% names(all_results)) {
rin_models <- all_results[["SMRIN"]]
rin_r_values <- sapply(rin_models, function(x) {
if(is.null(x)) return(NA)
return(x$performance$pooled_r)
# return(sqrt(x$performance$pooled_r2)) # use when required for R²
})
rin_r_values <- rin_r_values[!is.na(rin_r_values)]
cat("\nRIN Score Prediction:\n")
cat(paste(" Successful models:", length(rin_r_values), "\n"))
cat(paste(" Mean R:", round(mean(rin_r_values), 3), "\n"))
cat(paste(" Median R:", round(median(rin_r_values), 3), "\n"))
cat(paste(" Strong correlations (R > 0.5):", sum(rin_r_values > 0.5), "\n"))
}
# Autolysis Models
if("SMATSSCR" %in% names(all_results)) {
auto_models <- all_results[["SMATSSCR"]]
auto_r_values <- sapply(auto_models, function(x) {
if(is.null(x)) return(NA)
return(sqrt(x$performance$pooled_r2))
})
auto_r_values <- auto_r_values[!is.na(auto_r_values)]
cat("\nAutolysis Score Prediction:\n")
cat(paste(" Successful models:", length(auto_r_values), "\n"))
cat(paste(" Mean R:", round(mean(auto_r_values), 3), "\n"))
cat(paste(" Median R:", round(median(auto_r_values), 3), "\n"))
cat(paste(" Strong correlations (R > 0.5):", sum(auto_r_values > 0.5), "\n"))
}
cat("\nFEATURE ANALYSIS:\n")
cat(paste(" Common features between models:", length(common_features), "\n"))
cat(paste(" RIN-specific features:", length(rin_only_features), "\n"))
cat(paste(" Autolysis-specific features:", length(auto_only_features), "\n"))
cat("\nKEY FINDINGS:\n")
cat("1. Tissue-specific models show heterogeneous predictability\n")
cat("2. Feature importance varies across tissues and quality metrics\n")
cat("3. Cross-validation demonstrates model robustness\n")
cat("\n=================================\n")
cat("Report generated on:", as.character(Sys.time()), "\n")
sink()
message("Analysis complete! Report saved to:", report_path)---
title: "R Notebook"
output: html_notebook
---

#===============================================================================
# GTEx Tissue-Level Modeling Pipeline
# Purpose: Tissue-specific quality prediction using Lasso regression
#===============================================================================

#------------------------------------------------------------------------------
# 1. Setup
#------------------------------------------------------------------------------
```{r}
# Load data once
if(!exists("merged_gtex")) {
  merged_gtex <- readRDS(file.path(data_path, "processed/merged_gtex.rds"))
}
```

#------------------------------------------------------------------------------
# 2. Within-Group vs Between-Group Analysis for Single Tissue
#------------------------------------------------------------------------------
```{r}
# Analyze a specific tissue
tissue_name <- "Esophagus"
tissue_data <- merged_gtex[tissue_main == tissue_name]
subtypes <- unique(tissue_data$tissue_sub)

if(length(subtypes) > 1 && nrow(tissue_data) >= 10) {
  # Extract and scale features
  feature_cols <- grep("^feature_", names(tissue_data), value = TRUE)
  feature_matrix <- scale(as.matrix(tissue_data[, ..feature_cols]))
  
  # Calculate distance matrix
  dist_matrix <- dist(feature_matrix)
  
  # Calculate within-group and between-group distances
  within_distances <- list()
  between_distances <- list()
  
  for(subtype in subtypes) {
    subtype_indices <- which(tissue_data$tissue_sub == subtype)
    
    if(length(subtype_indices) > 1) {
      # Within-group distances
      subtype_dist <- as.matrix(dist_matrix)[subtype_indices, subtype_indices]
      within_distances[[subtype]] <- subtype_dist[upper.tri(subtype_dist)]
      
      # Between-group distances
      other_indices <- which(tissue_data$tissue_sub != subtype)
      if(length(other_indices) > 0) {
        between_dists <- as.matrix(dist_matrix)[subtype_indices, other_indices]
        between_distances[[subtype]] <- as.vector(between_dists)
      }
    }
  }
  
  # Calculate summary statistics
  all_within <- unlist(within_distances)
  all_between <- unlist(between_distances)
  
  similarity_results <- list(
    tissue = tissue_name,
    subtypes = subtypes,
    samples_per_subtype = table(tissue_data$tissue_sub),
    summary = data.frame(
      comparison = c("Within Subtype", "Between Subtypes"),
      mean_distance = c(mean(all_within), mean(all_between)),
      median_distance = c(median(all_within), median(all_between))
    ),
    ratio = mean(all_between) / mean(all_within)
  )
  
  # PERMANOVA test
  perm_test <- adonis2(dist_matrix ~ tissue_sub, data = tissue_data)
  statistical_results <- list(
    tissue = tissue_name,
    permanova = perm_test,
    r_squared = perm_test$R2[1],
    p_value = perm_test$`Pr(>F)`[1],
    significant = perm_test$`Pr(>F)`[1] < 0.05
  )
  
  # Create visualizations
  pca_result <- prcomp(feature_matrix)
  pca_plot <- ggplot(data.frame(PC1 = pca_result$x[,1], PC2 = pca_result$x[,2], 
                                Subtype = tissue_data$tissue_sub), 
                     aes(x = PC1, y = PC2, color = Subtype)) +
    geom_point(alpha = 0.7, size = 3) +
    theme_minimal() +
    labs(title = paste("PCA of", tissue_name, "by Subtype"))
  
  ggsave(file.path(output_path, paste0(tissue_name, "_subtype_pca.pdf")), 
         pca_plot, width = 10, height = 8, dpi = 300)
  
  # UMAP visualization
  set.seed(123)
  umap_result <- uwot::umap(feature_matrix, n_neighbors = min(15, nrow(feature_matrix)-1))
  
  umap_plot <- ggplot(data.frame(UMAP1 = umap_result[,1], UMAP2 = umap_result[,2],
                                 Subtype = tissue_data$tissue_sub),
                     aes(x = UMAP1, y = UMAP2, color = Subtype)) +
    geom_point(alpha = 0.7, size = 3) +
    theme_minimal() +
    labs(title = paste("UMAP of", tissue_name, "by Subtype"))
  
  ggsave(file.path(output_path, paste0(tissue_name, "_subtype_umap.pdf")), 
         umap_plot, width = 10, height = 8, dpi = 300)
  
  # Save results
  saveRDS(similarity_results, file.path(results_path, paste0(tissue_name, "_distance_analysis.rds")))
  saveRDS(statistical_results, file.path(results_path, paste0(tissue_name, "_statistical_tests.rds")))
}
```

#------------------------------------------------------------------------------
# 3. Tissue-Level Lasso Modeling
#------------------------------------------------------------------------------
```{r}
# Set parameters
k_folds <- 5
targets <- c("SMRIN", "SMATSSCR")
tissues <- unique(merged_gtex$tissue_main)
all_results <- list()

# Process each target
for(target in targets) {
  target_results <- list()
  message(paste("\nProcessing target:", target))
  
  # Process each tissue
  for(tissue_name in tissues) {
    message(paste("\nAnalyzing", tissue_name, "for", target))
    
    # Extract and filter data
    tissue_data <- merged_gtex[tissue_main == tissue_name]
    tissue_data <- tissue_data[!is.na(tissue_data[[target]])]
    
    if(nrow(tissue_data) < 10) {
      message(paste("Insufficient samples for", tissue_name))
      next
    }
    
    # Feature selection
    feature_cols <- grep("^feature_", names(tissue_data), value = TRUE)
    feature_vars <- apply(tissue_data[, ..feature_cols], 2, var)
    valid_features <- feature_cols[feature_vars > 1e-10]
    
    # Create cross-validation folds
    set.seed(123)
    folds <- createFolds(tissue_data[[target]], k = k_folds)
    
    # Cross-validation
    fold_results <- list()
    all_fold_predictions <- data.frame()
    
    for(i in 1:k_folds) {
      # Split data
      test_indices <- folds[[i]]
      train_data <- tissue_data[-test_indices]
      test_data <- tissue_data[test_indices]
      
      # Feature selection - top 5% correlated
      feature_correlations <- sapply(valid_features, function(col) {
        cor(train_data[[col]], train_data[[target]], use = "complete.obs")
      })
      
      top_n_features <- ceiling(length(valid_features) * 0.05)
      top_features <- names(sort(abs(feature_correlations), decreasing = TRUE))[1:top_n_features]
      
      # Prepare matrices
      x_train <- as.matrix(train_data[, ..top_features])
      y_train <- train_data[[target]]
      x_train_scaled <- scale(x_train)
      x_mean <- attr(x_train_scaled, "scaled:center")
      x_sd <- attr(x_train_scaled, "scaled:scale")
      
      # Train model
      cv_fit <- cv.glmnet(x_train_scaled, y_train, alpha = 1)
      best_lambda <- cv_fit$lambda.min
      lasso_model <- glmnet(x_train_scaled, y_train, alpha = 1, lambda = best_lambda)
      
      # Make predictions
      x_test <- as.matrix(test_data[, ..top_features])
      x_test_scaled <- scale(x_test, center = x_mean, scale = x_sd)
      predictions <- predict(lasso_model, x_test_scaled, s = best_lambda)
      
      # Store predictions
      fold_predictions <- data.frame(
        fold = i,
        sample_id = test_data$sample_id,
        actual = test_data[[target]],
        predicted = as.numeric(predictions)
      )
      all_fold_predictions <- rbind(all_fold_predictions, fold_predictions)
      
      # Calculate metrics
      rmse <- sqrt(mean((predictions - test_data[[target]])^2))
      r_value <- cor(predictions, test_data[[target]]) 
      r2 <- cor(predictions, test_data[[target]])^2
      mae <- mean(abs(predictions - test_data[[target]]))
      
      fold_results[[i]] <- list(
        fold = i,
        rmse = rmse,
        r = r_value,
        r2 = r2,
        mae = mae,
        lambda = best_lambda,
        model = lasso_model,
        features = top_features
      )
    }
    
    # Calculate overall performance
    pooled_rmse <- sqrt(mean((all_fold_predictions$predicted - all_fold_predictions$actual)^2))
    pooled_r <- cor(all_fold_predictions$predicted, all_fold_predictions$actual)  # <-- R, not R²
    pooled_r2 <- cor(all_fold_predictions$predicted, all_fold_predictions$actual)^2
    pooled_mae <- mean(abs(all_fold_predictions$predicted - all_fold_predictions$actual))
    
    # Count feature selection frequency
    all_selected_features <- unlist(lapply(fold_results, function(x) x$features))
    feature_counts <- table(all_selected_features)
    consistent_features <- names(feature_counts[feature_counts >= 3])
    
    # Store results
    tissue_model_result <- list(
      tissue = tissue_name,
      target = target,
      performance = list(
        pooled_rmse = pooled_rmse,
        pooled_r = pooled_r,
        pooled_r2 = pooled_r2,
        pooled_mae = pooled_mae
      ),
      fold_results = fold_results,
      feature_counts = feature_counts,
      consistent_features = consistent_features,
      sample_size = nrow(tissue_data),
      all_predictions = all_fold_predictions
    )
    
    target_results[[tissue_name]] <- tissue_model_result
    
    # Create visualization for good models (using R > 0.55 which is approximately R² > 0.3)
  # if(pooled_r2 > 0.3) {  # use if R² is required
    if(abs(pooled_r) > 0.55) {
      scatter_plot <- ggplot(all_fold_predictions, aes(x = actual, y = predicted)) +
        geom_point(alpha = 0.6, color = "darkblue", size = 2) +
        geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") +
        geom_smooth(method = "lm", se = TRUE, color = "blue") +
        labs(
          title = paste(tissue_name, "-", target, "Prediction"),
          subtitle = paste("R =", round(pooled_r, 3), "RMSE =", round(pooled_rmse, 3)),
        # subtitle = paste("R² =", round(pooled_r2, 3), "RMSE =", round(pooled_rmse, 3)), # use if R² is required
          x = paste("Actual", target),
          y = paste("Predicted", target)
        ) +
        theme_minimal()
      
      ggsave(file.path(output_path, paste0(tissue_name, "_", target, "_scatter.pdf")), 
             scatter_plot, width = 10, height = 6, dpi = 300)
    }
  }
  
  all_results[[target]] <- target_results
  saveRDS(target_results, file.path(results_path, paste0(target, "_model_results.rds")))
}

saveRDS(all_results, file.path(results_path, "all_tissue_models_complete.rds"))
```

#------------------------------------------------------------------------------
# 4. Model Performance Summary
#------------------------------------------------------------------------------
```{r}
# Create performance summary
create_performance_summary <- function(results, target_name) {
  summary_df <- data.frame()
  
  for(tissue in names(results)) {
    tissue_result <- results[[tissue]]
    if(is.null(tissue_result)) next
    
    row <- data.frame(
      Tissue = tissue,
      Samples = tissue_result$sample_size,
      RMSE = round(tissue_result$performance$pooled_rmse, 3),
      R = round(tissue_result$performance$pooled_r, 3),
    # R_squared = round(tissue_result$performance$pooled_r2, 3), # use if R² is required
      MAE = round(tissue_result$performance$pooled_mae, 3),
      Consistent_Features = length(tissue_result$consistent_features)
    )
    summary_df <- rbind(summary_df, row)
  }
  
  summary_df <- summary_df[order(abs(summary_df$R), decreasing = TRUE),]  # Sort by absolute R
  summary_df$Performance_Category <- cut(abs(summary_df$R),  # Use absolute R for categories
                                      breaks = c(-Inf, 0.316, 0.548, 0.707, Inf), 
                                      labels = c("Poor", "Moderate", "Good", "Excellent"))  

# summary_df <- summary_df[order(summary_df$R_squared, decreasing = TRUE),] # Sort by R²
# summary_df$Performance_Category <- cut(summary_df$R_squared, # Use R² for categories
                                  #  breaks = c(-Inf, 0.1, 0.3, 0.5, Inf), 
                                  #  labels = c("Poor", "Moderate", "Good", "Excellent"))
  
  return(summary_df)
}

# Create summaries
rin_summary <- create_performance_summary(all_results[["SMRIN"]], "RIN Score")
autolysis_summary <- create_performance_summary(all_results[["SMATSSCR"]], "Autolysis Score")

# Save summaries
write.csv(rin_summary, file.path(results_path, "rin_model_summary.csv"), row.names = FALSE)
write.csv(autolysis_summary, file.path(results_path, "autolysis_model_summary.csv"), row.names = FALSE)

# Create comparison
model_comparison <- merge(
  rin_summary[, c("Tissue", "R")],
  autolysis_summary[, c("Tissue", "R")],
  by = "Tissue",
  suffixes = c("_RIN", "_Auto")
)
model_comparison$Better_Model <- ifelse(abs(model_comparison$R_RIN) > abs(model_comparison$R_Auto), 
                                      "RIN", "Autolysis")

# Create comparison when if required for R²
# model_comparison <- merge(
#   rin_summary[, c("Tissue", "R_squared")],
#   autolysis_summary[, c("Tissue", "R_squared")],
#   by = "Tissue",
#   suffixes = c("_RIN", "_Auto")
# )
# model_comparison$Better_Model <- ifelse(model_comparison$R_squared_RIN > model_comparison$R_squared_Auto, 
#                                       "RIN", "Autolysis")
write.csv(model_comparison, file.path(results_path, "model_comparison.csv"), row.names = FALSE)
```

#------------------------------------------------------------------------------
# 5. Figure 3 - Model Performance Visualization
#------------------------------------------------------------------------------
```{r}
# Panel A & B: Performance bar plots
create_horizontal_barplot <- function(all_results, score_type, panel_label) {
  r_values <- sapply(all_results[[score_type]], function(x) {
    if(is.null(x)) return(0)
    return(x$performance$pooled_r)
  # return(sqrt(x$performance$pooled_r2)) # use when required for R²
  })
  
  plot_data <- data.frame(
    Tissue = names(r_values),
    R = r_values
  )[order(-r_values), ]
  
  plot_data$Tissue <- factor(plot_data$Tissue, levels = rev(plot_data$Tissue))
  
  if(score_type == "SMRIN") {
    color_gradient <- colorRampPalette(c("#E8F5E8", "#66cdaa", "#2E8B57"))(100)
  } else {
    color_gradient <- colorRampPalette(c("#FFF2E6", "#FC8D62", "#D94801"))(100)
  }
  
  bar_plot <- ggplot(plot_data, aes(x = R, y = Tissue, fill = R)) +
    geom_bar(stat = "identity", width = 0.8) +
    geom_text(aes(label = sprintf("%.3f", R), x = R/2), 
              hjust = 0.5, size = 3.5, fontface = "bold") +
    scale_fill_gradientn(colors = color_gradient, guide = "none") +
    theme_minimal() +
    labs(title = panel_label, x = "Correlation Coefficient (R)", y = "") +
    theme(
      plot.title = element_text(size = 20, face = "bold", hjust = 0),
      axis.title.x = element_text(size = 14, face = "bold"),
      axis.text.y = element_text(size = 13, face = "bold")
    )
  
  return(bar_plot)
}

rin_barplot <- create_horizontal_barplot(all_results, "SMRIN", "A")
autolysis_barplot <- create_horizontal_barplot(all_results, "SMATSSCR", "B")

# Panel C: RIN scatter plots
create_scatter_panel <- function(all_results, top_tissues) {
  plots <- list()
  
  for(i in 1:3) {
    result <- all_results[["SMRIN"]][[top_tissues[i]]]
    if(is.null(result)) next
    
    all_preds <- result$all_predictions
    r_value <- result$performance$pooled_r
  # r_value <- sqrt(result$performance$pooled_r2) # use when required for R²
    rmse_value <- result$performance$pooled_rmse
    
    p <- ggplot(all_preds, aes(x = actual, y = predicted)) +
      geom_point(alpha = 0.7, color = "#66cdaa", size = 1.5) +
      geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") +
      geom_smooth(method = "lm", se = TRUE, color = "blue") +
      ggtitle(paste0(top_tissues[i], " | R = ", round(r_value, 3))) +
      theme_minimal()
    
    plots[[i]] <- p
  }
  
  return(plots)
}

# Get top 3 tissues
all_r_rin <- sapply(all_results[["SMRIN"]], function(x) {
  if(is.null(x)) return(0)
  return(abs(x$performance$pooled_r))  # Use absolute value for sorting
# return(sqrt(x$performance$pooled_r2)) # use when required for R²
})
top_rin_tissues <- names(sort(all_r_rin, decreasing = TRUE))[1:3]

rin_scatter_plots <- create_scatter_panel(all_results, top_rin_tissues)
rin_grid <- grid.arrange(grobs = rin_scatter_plots, nrow = 1,
                        bottom = textGrob("Actual RIN Score", gp = gpar(fontsize = 18, fontface = "bold")),
                        left = textGrob("Predicted RIN Score", rot = 90, gp = gpar(fontsize = 18, fontface = "bold")),
                        top = textGrob("C", x = 0.02, just = "left", gp = gpar(fontsize = 18, fontface = "bold")))

# Panel D: Autolysis box plots
create_boxplot_panel <- function(all_results, top_tissues) {
  plots <- list()
  
  for(i in 1:3) {
    result <- all_results[["SMATSSCR"]][[top_tissues[i]]]
    if(is.null(result)) next
    
    all_preds <- result$all_predictions
    all_preds$actual_factor <- factor(all_preds$actual, levels = c(0, 1, 2, 3))
    r_value <- sqrt(result$performance$pooled_r2)
    rmse_value <- result$performance$pooled_rmse
    
    p <- ggplot(all_preds, aes(x = actual_factor, y = predicted)) +
      geom_boxplot(aes(fill = actual_factor), outlier.shape = NA) +
      geom_jitter(width = 0.2, height = 0, alpha = 0.2, color = "#21908d") +
      scale_fill_manual(values = c("0" = "#e0e0e0", "1" = "#bdd7e7", 
                                 "2" = "#9ecae1", "3" = "#6baed6")) +
      ggtitle(paste0(top_tissues[i], " | R = ", round(r_value, 3))) +
      theme_minimal() +
      theme(legend.position = "none")
    
    plots[[i]] <- p
  }
  
  return(plots)
}

all_r_auto <- sapply(all_results[["SMATSSCR"]], function(x) {
  if(is.null(x)) return(0)
  return(sqrt(x$performance$pooled_r2))
})
top_auto_tissues <- names(sort(all_r_auto, decreasing = TRUE))[1:3]

auto_boxplot_plots <- create_boxplot_panel(all_results, top_auto_tissues)
autolysis_grid <- grid.arrange(grobs = auto_boxplot_plots, nrow = 1,
                              bottom = textGrob("Actual Category", gp = gpar(fontsize = 18, fontface = "bold")),
                              left = textGrob("Predicted Score", rot = 90, gp = gpar(fontsize = 18, fontface = "bold")),
                              top = textGrob("D", x = 0.02, just = "left", gp = gpar(fontsize = 18, fontface = "bold")))

# Combine all panels
combined_barplots <- grid.arrange(rin_barplot, autolysis_barplot, ncol = 2)
combined_CD_plots <- grid.arrange(rin_grid, autolysis_grid, nrow = 2)
figure3_final <- grid.arrange(combined_barplots, combined_CD_plots, ncol = 2, widths = c(0.45, 0.55))

ggsave(file.path(output_path, "Figure3_Final_Complete.pdf"), 
       figure3_final, width = 20, height = 12, dpi = 600, bg = "white")
```

#------------------------------------------------------------------------------
# 6. Figure 4 - Feature Importance
#------------------------------------------------------------------------------
```{r}
# Extract top features
extract_features <- function(results, target, top_n = 20) {
  all_tissue_features <- data.frame()
  
  for(tissue in names(results[[target]])) {
    tissue_result <- results[[target]][[tissue]]
    if(is.null(tissue_result)) next
    
    if(!is.null(tissue_result$feature_counts)) {
      features <- names(tissue_result$feature_counts)
      counts <- as.numeric(tissue_result$feature_counts)
      
      tissue_df <- data.frame(
        feature = features,
        count = counts,
        tissue = tissue,
        stringsAsFactors = FALSE
      )
      all_tissue_features <- rbind(all_tissue_features, tissue_df)
    }
  }
  
  feature_summary <- all_tissue_features %>%
    group_by(feature) %>%
    summarize(total_count = sum(count)) %>%
    arrange(desc(total_count)) %>%
    slice(1:top_n)
  
  feature_matrix <- all_tissue_features %>%
    filter(feature %in% feature_summary$feature) %>%
    reshape2::dcast(feature ~ tissue, value.var = "count", fill = 0)
  
  return(list(summary = feature_summary, matrix = feature_matrix))
}

rin_features <- extract_features(all_results, "SMRIN")
auto_features <- extract_features(all_results, "SMATSSCR")

# Find common features
common_features <- intersect(rin_features$summary$feature, auto_features$summary$feature)
rin_only_features <- setdiff(rin_features$summary$feature, auto_features$summary$feature)
auto_only_features <- setdiff(auto_features$summary$feature, rin_features$summary$feature)

# Create heatmaps
create_heatmap <- function(feature_data, color_palette) {
  feature_long <- reshape2::melt(feature_data$matrix, id.vars = "feature",
                               variable.name = "tissue", value.name = "count")
  
  feature_long <- merge(feature_long, feature_data$summary[, c("feature", "total_count")], by = "feature")
  feature_long$feature_label <- sub("feature_", "", feature_long$feature)
  
  p <- ggplot(feature_long, aes(x = tissue, y = reorder(feature_label, total_count))) +
    geom_tile(aes(fill = count), color = "white", size = 0.1) +
    scale_fill_gradientn(colors = color_palette, name = "Selection\nFrequency") +
    labs(x = NULL, y = "Feature ID") +
    theme_minimal() +
    theme(
      axis.text.x = element_text(angle = 65, hjust = 1, size = 12, face = "bold"),
      axis.text.y = element_text(size = 12, face = "bold"),
      panel.grid = element_blank()
    )
  
  return(p)
}

rin_heatmap <- create_heatmap(rin_features, 
                             colorRampPalette(c("white", "#66CDAA", "#006400"))(100)) +
  ggtitle("A")

auto_heatmap <- create_heatmap(auto_features,
                              colorRampPalette(c("white", "#FC8D62", "#D94801"))(100)) +
  ggtitle("B")

# Create Venn diagram
venn_plot <- ggplot() +
  ggforce::geom_circle(data = data.frame(x = c(-1, 1), y = c(0, 0), r = c(2, 2),
                                        category = c("RIN Features", "Autolysis Features")),
                      aes(x0 = x, y0 = y, r = r, fill = category),
                      alpha = 0.5, color = "black", size = 0.8) +
  geom_text(data = data.frame(x = c(-1.65, 0, 1.65), y = c(0, 0, 0),
                              label = c(length(rin_only_features), 
                                       length(common_features),
                                       length(auto_only_features))),
           aes(x = x, y = y, label = label), size = 10, fontface = "bold") +
  scale_fill_manual(values = c("RIN Features" = "#66CDAA", "Autolysis Features" = "#FC8D62")) +
  theme_void() +
  theme(legend.position = "none") +
  coord_fixed() +
  xlim(-3, 3) + ylim(-2, 2) +
  ggtitle("C")

# Combine plots
heatmaps <- plot_grid(rin_heatmap, auto_heatmap, ncol = 2)
figure4 <- plot_grid(heatmaps, venn_plot, nrow = 2, rel_heights = c(2, 1.5))

ggsave(file.path(output_path, "Figure4_Feature.pdf"), 
       figure4, width = 14, height = 12, dpi = 600, bg = "transparent")
```


#------------------------------------------------------------------------------
# 7. Additional Visualizations and Comparative Analysis
#------------------------------------------------------------------------------
```{r}
# Create comparative performance chart for top tissues
create_comparative_bar_chart <- function(rin_results, autolysis_results) {
  common_tissues <- intersect(rin_results$Tissue, autolysis_results$Tissue)
  
  comparison_data <- data.frame(
    Tissue = common_tissues,
    RIN_R = rin_results$R[match(common_tissues, rin_results$Tissue)],
    Autolysis_R = autolysis_results$R[match(common_tissues, autolysis_results$Tissue)]
  )
  
  comparison_data$Average_R <- (abs(comparison_data$RIN_R) + abs(comparison_data$Autolysis_R)) / 2
  comparison_data <- comparison_data[order(-comparison_data$Average_R), ]
  top_tissues <- head(comparison_data, 15)
  
  plot_data <- reshape2::melt(top_tissues[, c("Tissue", "RIN_R", "Autolysis_R")], 
                             id.vars = "Tissue", variable.name = "Model", value.name = "R_value")
  plot_data$Tissue <- factor(plot_data$Tissue, levels = top_tissues$Tissue)
  
  comparison_plot <- ggplot(plot_data, aes(x = Tissue, y = R_value, fill = Model)) +
    geom_bar(stat = "identity", position = "dodge", alpha = 0.8) +
    geom_text(aes(label = sprintf("%.3f", R_value)), 
             position = position_dodge(width = 0.9), vjust = -0.3, size = 3) +
    scale_fill_manual(values = c("RIN_R" = "#66CDAA", "Autolysis_R" = "#FC8D62"),
                     labels = c("RIN", "Autolysis")) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(angle = 45, hjust = 1, size = 11, face = "bold"),
      plot.title = element_text(size = 16, face = "bold"),
      legend.position = "top"
    ) +
    labs(
      title = "Model Performance Comparison Across Top Tissues",
      subtitle = "Top 15 tissues ranked by average correlation coefficient",
      x = "Tissue Type",
      y = "Correlation Coefficient (R)",
      fill = "Model Type"
    )
  
  ggsave(file.path(output_path, "comparative_performance_chart.pdf"), 
         comparison_plot, width = 14, height = 8, dpi = 300)
  
  return(comparison_plot)
}

# Create correlation distribution analysis
create_correlation_distribution <- function(results_df, target_name) {
  r_dist_plot <- ggplot(results_df, aes(x = abs(R))) +
    geom_histogram(binwidth = 0.05, fill = "steelblue", color = "white", alpha = 0.8) +
    geom_vline(aes(xintercept = mean(abs(R))), 
              color = "red", linetype = "dashed", size = 1.2) +
    geom_vline(aes(xintercept = median(abs(R))), 
              color = "darkgreen", linetype = "dashed", size = 1.2) +
    annotate("text", x = mean(abs(results_df$R)) + 0.08, y = max(table(cut(abs(results_df$R), 20))) * 0.8, 
             label = paste("Mean =", round(mean(abs(results_df$R)), 3)),
             color = "red", fontface = "bold") +
    annotate("text", x = median(abs(results_df$R)) - 0.08, y = max(table(cut(abs(results_df$R), 20))) * 0.6, 
             label = paste("Median =", round(median(abs(results_df$R)), 3)),
             color = "darkgreen", fontface = "bold") +
    theme_minimal() +
    labs(
      title = paste("Distribution of Correlation Coefficients:", target_name),
      subtitle = paste("Analysis of", nrow(results_df), "tissue-specific models"),
      x = "Absolute Correlation Coefficient |R|",
      y = "Frequency"
    ) +
    theme(
      plot.title = element_text(size = 14, face = "bold"),
      axis.title = element_text(size = 12, face = "bold")
    )
  
  ggsave(file.path(output_path, paste0(gsub(" ", "_", tolower(target_name)), "_correlation_distribution.pdf")), 
         r_dist_plot, width = 10, height = 7, dpi = 300)
  
  return(r_dist_plot)
}

# Create enhanced scatter plots with residual analysis
create_enhanced_scatter_plots <- function(results, target_name, top_n = 6) {
  all_r_values <- sapply(results, function(x) {
    if(is.null(x)) return(0)
    return(abs(x$performance$pooled_r))
  })
  
  top_tissues <- names(sort(all_r_values, decreasing = TRUE))[1:min(top_n, length(all_r_values))]
  
  for(tissue in top_tissues) {
    if(is.null(results[[tissue]])) next
    
    predictions <- results[[tissue]]$all_predictions
    r_value <- results[[tissue]]$performance$pooled_r
    rmse_value <- results[[tissue]]$performance$pooled_rmse
    
    predictions$residual <- predictions$predicted - predictions$actual
    
    scatter_plot <- ggplot(predictions, aes(x = actual, y = predicted)) +
      geom_point(alpha = 0.7, color = "#2E86AB", size = 2.5) +
      geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed", size = 1) +
      geom_smooth(method = "lm", se = TRUE, color = "darkblue", alpha = 0.3) +
      labs(
        title = paste(tissue, "-", target_name, "Prediction Model"),
        subtitle = paste("R =", round(r_value, 3), "| RMSE =", round(rmse_value, 3)),
        x = paste("Actual", target_name),
        y = paste("Predicted", target_name)
      ) +
      theme_minimal() +
      theme(
        plot.title = element_text(size = 14, face = "bold"),
        plot.subtitle = element_text(size = 12),
        axis.title = element_text(size = 11, face = "bold")
      )
    
    ggsave(file.path(output_path, paste0("enhanced_", tissue, "_", gsub(" ", "_", tolower(target_name)), "_scatter.pdf")), 
           scatter_plot, width = 10, height = 8, dpi = 300)
  }
}

# Execute comparative visualizations
comparison_chart <- create_comparative_bar_chart(rin_summary, autolysis_summary)
rin_distribution <- create_correlation_distribution(rin_summary, "RIN Score")
autolysis_distribution <- create_correlation_distribution(autolysis_summary, "Autolysis Score")

create_enhanced_scatter_plots(all_results[["SMRIN"]], "RIN Score", top_n = 6)
create_enhanced_scatter_plots(all_results[["SMATSSCR"]], "Autolysis Score", top_n = 6)
```


#------------------------------------------------------------------------------
# 8. Figure 5: Model Performance vs Sample Size Analysis
#------------------------------------------------------------------------------
```{r}
create_sample_size_analysis <- function(rin_summary, autolysis_summary) {
  
  combined_data <- rbind(
    data.frame(
      Tissue = rin_summary$Tissue,
      Samples = rin_summary$Samples, 
      R_value = abs(rin_summary$R),
      Model = "RIN"
    ),
    data.frame(
      Tissue = autolysis_summary$Tissue,
      Samples = autolysis_summary$Samples,
      R_value = abs(autolysis_summary$R),
      Model = "Autolysis"
    )
  )
  
  scatter_panel <- ggplot(combined_data, aes(x = Samples, y = R_value, color = Model)) +
    geom_point(size = 3.5, alpha = 0.8) +
    geom_smooth(method = "lm", se = TRUE, alpha = 0.3, size = 1.2) +
    scale_color_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
    theme_minimal() +
    labs(
      title = "A",
      x = "Sample Size (n)",
      y = "Correlation Coefficient |R|",
      subtitle = "Relationship between sample size and model performance"
    ) +
    theme(
      plot.title = element_text(size = 20, face = "bold", hjust = 0),
      plot.subtitle = element_text(size = 12),
      legend.position = "top",
      legend.title = element_text(size = 12, face = "bold"),
      axis.title = element_text(size = 12, face = "bold")
    )
  
  combined_data$size_category <- cut(combined_data$Samples, 
                                   breaks = c(0, 50, 100, 200, 500, Inf),
                                   labels = c("≤50", "51-100", "101-200", "201-500", ">500"))
  
  bin_summary <- combined_data %>%
    group_by(size_category, Model) %>%
    summarise(
      mean_r = mean(R_value, na.rm = TRUE),
      se_r = sd(R_value, na.rm = TRUE) / sqrt(n()),
      tissue_count = n(),
      .groups = 'drop'
    )
  
  bin_panel <- ggplot(bin_summary, aes(x = size_category, y = mean_r, fill = Model)) +
    geom_bar(stat = "identity", position = "dodge", alpha = 0.85, width = 0.7) +
    geom_errorbar(aes(ymin = mean_r - se_r, ymax = mean_r + se_r),
                  position = position_dodge(width = 0.7), width = 0.25, size = 0.8) +
    geom_text(aes(label = tissue_count), 
              position = position_dodge(width = 0.7), 
              vjust = -1.5, size = 3.5, fontface = "bold") +
    scale_fill_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
    theme_minimal() +
    labs(
      title = "B",
      x = "Sample Size Categories",
      y = "Mean |R| ± SE",
      subtitle = "Performance stratified by sample size (numbers show tissue count)"
    ) +
    theme(
      plot.title = element_text(size = 20, face = "bold", hjust = 0),
      plot.subtitle = element_text(size = 12),
      legend.position = "none",
      axis.title = element_text(size = 12, face = "bold")
    )
  
  figure5_combined <- grid.arrange(scatter_panel, bin_panel, ncol = 2, widths = c(1, 1))
  
  return(figure5_combined)
}

analyze_sample_size_effects <- function(combined_data) {
  rin_data <- combined_data[combined_data$Model == "RIN", ]
  auto_data <- combined_data[combined_data$Model == "Autolysis", ]
  
  rin_cor <- cor.test(rin_data$Samples, rin_data$R_value)
  auto_cor <- cor.test(auto_data$Samples, auto_data$R_value)
  
  return(list(rin_test = rin_cor, autolysis_test = auto_cor))
}

figure5_plot <- create_sample_size_analysis(rin_summary, autolysis_summary)
sample_size_stats <- analyze_sample_size_effects(rbind(
  data.frame(Tissue = rin_summary$Tissue, Samples = rin_summary$Samples, 
             R_value = abs(rin_summary$R), Model = "RIN"),
  data.frame(Tissue = autolysis_summary$Tissue, Samples = autolysis_summary$Samples,
             R_value = abs(autolysis_summary$R), Model = "Autolysis")
))

ggsave(file.path(output_path, "Figure5_Sample_Size_Analysis.pdf"), 
       figure5_plot, width = 16, height = 8, dpi = 600, bg = "white")
```

#------------------------------------------------------------------------------
# 9. Top 5 Tissues Quality Overlay
#------------------------------------------------------------------------------
```{r}
# Get top 5 tissues for each score
get_top5_tissues <- function(target_name) {
  r_values <- sapply(all_results[[target_name]], function(x) {
    if(is.null(x) || is.null(x$performance$pooled_r2)) return(0)
  # return(sqrt(x$performance$pooled_r2)) # use when required for R²
    return(abs(x$performance$pooled_r))  # Use absolute R for ranking
  })
  
  r_values <- r_values[r_values > 0]
  top5 <- sort(r_values, decreasing = TRUE)[1:5]
  
  return(data.frame(
    tissue = names(top5),
    correlation_r = as.numeric(top5)
  ))
}

top5_rin <- get_top5_tissues("SMRIN")
top5_auto <- get_top5_tissues("SMATSSCR")

# Create overlay plots
create_simple_overlay <- function(tissue_name, score_type, color_palette) {
  tissue_data <- merged_gtex[tissue_main == tissue_name]
  
  if(nrow(tissue_data) < 10) {
    return(ggplot() + theme_void())
  }
  
  # Get features and run UMAP
  feature_cols <- grep("^feature_", names(tissue_data), value = TRUE)
  feature_matrix <- as.matrix(tissue_data[, ..feature_cols])
  feature_matrix <- scale(feature_matrix[, apply(feature_matrix, 2, var) > 1e-10])
  
  set.seed(123)
  umap_result <- umap(feature_matrix, n_neighbors = min(15, nrow(feature_matrix) - 1))
  
  plot_data <- data.frame(
    UMAP1 = umap_result[, 1],
    UMAP2 = umap_result[, 2],
    Score = tissue_data[[score_type]]
  )
  
  # Separate data with and without scores
  data_with_scores <- plot_data[!is.na(plot_data$Score), ]
  data_missing_scores <- plot_data[is.na(plot_data$Score), ]
  
  # Get R value
  tissue_result <- all_results[[score_type]][[tissue_name]]
  r_value <- if(!is.null(tissue_result)) tissue_result$performance$pooled_r else 0
# r_value <- if(!is.null(tissue_result)) sqrt(tissue_result$performance$pooled_r2) else 0 # use when required for R²
  
  p <- ggplot() +
    geom_point(data = data_missing_scores, aes(x = UMAP1, y = UMAP2), 
               color = "lightgrey", alpha = 0.5, size = 1.5) +
    geom_point(data = data_with_scores, aes(x = UMAP1, y = UMAP2, color = Score), 
               alpha = 0.7, size = 2) +
    color_palette +
    theme_minimal() +
    labs(
      title = tissue_name,
      subtitle = paste("R =", sprintf("%.3f", r_value)),
      x = "UMAP1", y = "UMAP2"
    )
  
  return(p)
}

# Define color palettes
rin_palette <- scale_color_viridis_c(name = "RIN\nScore", option = "viridis")
auto_palette <- scale_color_viridis_c(name = "Autolysis\nScore", option = "plasma")

# Create plots
rin_plots <- lapply(1:5, function(i) {
  create_simple_overlay(top5_rin$tissue[i], "SMRIN", rin_palette)
})

auto_plots <- lapply(1:5, function(i) {
  create_simple_overlay(top5_auto$tissue[i], "SMATSSCR", auto_palette)
})

# Combine panels
panel_a <- grid.arrange(grobs = rin_plots, nrow = 1,
                       top = textGrob("A", gp = gpar(fontsize = 20, fontface = "bold"),
                                     x = 0.02, just = "left"))

panel_b <- grid.arrange(grobs = auto_plots, nrow = 1,
                       top = textGrob("B", gp = gpar(fontsize = 20, fontface = "bold"),
                                     x = 0.02, just = "left"))

final_plot <- grid.arrange(panel_a, panel_b, nrow = 2)

ggsave(file.path(output_path, "Top5_Tissues_Quality_Overlay_AB.pdf"),
       final_plot, width = 20, height = 12, dpi = 300, bg = "white")
```

#------------------------------------------------------------------------------
# 10. Figure 6: Cross-Tissue Feature Consistency Analysis
#------------------------------------------------------------------------------
```{r}
create_feature_consistency_analysis <- function(all_results) {
  
  extract_consistent_features <- function(target_results, min_selections = 3) {
    tissue_features <- list()
    
    for(tissue in names(target_results)) {
      if(!is.null(target_results[[tissue]]$feature_counts)) {
        features <- names(target_results[[tissue]]$feature_counts)
        counts <- as.numeric(target_results[[tissue]]$feature_counts)
        selected_features <- features[counts >= min_selections]
        tissue_features[[tissue]] <- selected_features
      }
    }
    
    return(tissue_features)
  }
  
  rin_tissue_features <- extract_consistent_features(all_results[["SMRIN"]])
  auto_tissue_features <- extract_consistent_features(all_results[["SMATSSCR"]])
  
  create_consistency_heatmap <- function(tissue_features, title, color_scheme) {
    all_features <- unique(unlist(tissue_features))
    all_tissues <- names(tissue_features)
    
    selection_matrix <- matrix(0, nrow = length(all_features), ncol = length(all_tissues))
    rownames(selection_matrix) <- all_features
    colnames(selection_matrix) <- all_tissues
    
    for(tissue in all_tissues) {
      selected <- tissue_features[[tissue]]
      selection_matrix[selected, tissue] <- 1
    }
    
    consistency_scores <- rowSums(selection_matrix) / ncol(selection_matrix)
    top_features <- names(sort(consistency_scores, decreasing = TRUE))[1:25]
    plot_matrix <- selection_matrix[top_features, ]
    
    plot_data <- expand.grid(Feature = rownames(plot_matrix), 
                           Tissue = colnames(plot_matrix))
    plot_data$Selected <- as.vector(plot_matrix)
    plot_data$Consistency_Score <- consistency_scores[plot_data$Feature]
    plot_data$Feature_ID <- sub("feature_", "", plot_data$Feature)
    
    heatmap_plot <- ggplot(plot_data, aes(x = Tissue, y = reorder(Feature_ID, Consistency_Score))) +
      geom_tile(aes(fill = factor(Selected)), color = "white", size = 0.3) +
      scale_fill_manual(values = c("0" = "white", "1" = color_scheme), 
                       name = "Selected", labels = c("No", "Yes")) +
      theme_minimal() +
      theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 9, face = "bold"),
        axis.text.y = element_text(size = 9),
        panel.grid = element_blank(),
        legend.position = "right",
        plot.title = element_text(size = 16, face = "bold")
      ) +
      labs(
        title = title,
        x = NULL,
        y = "Feature ID"
      )
    
    return(heatmap_plot)
  }
  
  panel_a <- create_consistency_heatmap(rin_tissue_features, "A. RIN Model Feature Consistency", "#2E8B57")
  panel_b <- create_consistency_heatmap(auto_tissue_features, "B. Autolysis Model Feature Consistency", "#D2691E")
  
  calculate_stability_distribution <- function(tissue_features) {
    all_features <- unique(unlist(tissue_features))
    stability_scores <- sapply(all_features, function(feature) {
      tissues_with_feature <- sum(sapply(tissue_features, function(x) feature %in% x))
      return(tissues_with_feature / length(tissue_features))
    })
    return(stability_scores)
  }
  
  rin_stability <- calculate_stability_distribution(rin_tissue_features)
  auto_stability <- calculate_stability_distribution(auto_tissue_features)
  
  stability_comparison <- data.frame(
    Feature = c(names(rin_stability), names(auto_stability)),
    Stability = c(rin_stability, auto_stability),
    Model = rep(c("RIN", "Autolysis"), c(length(rin_stability), length(auto_stability)))
  )
  
  panel_c <- ggplot(stability_comparison, aes(x = Stability, fill = Model)) +
    geom_histogram(alpha = 0.75, bins = 15, position = "identity") +
    geom_vline(data = stability_comparison %>% group_by(Model) %>% summarise(mean_stab = mean(Stability)),
               aes(xintercept = mean_stab, color = Model), linetype = "dashed", size = 1.2) +
    scale_fill_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
    scale_color_manual(values = c("RIN" = "#2E8B57", "Autolysis" = "#D2691E")) +
    theme_minimal() +
    labs(
      title = "C. Feature Stability Distribution",
      x = "Stability Score (Fraction of Tissues)",
      y = "Number of Features",
      subtitle = "Distribution of cross-tissue feature consistency"
    ) +
    theme(
      plot.title = element_text(size = 16, face = "bold"),
      plot.subtitle = element_text(size = 12),
      legend.position = "top",
      axis.title = element_text(size = 11, face = "bold")
    )
  
  top_panels <- grid.arrange(panel_a, panel_b, ncol = 2)
  figure6_combined <- grid.arrange(top_panels, panel_c, nrow = 2, heights = c(2, 1))
  
  return(figure6_combined)
}

calculate_feature_overlap_stats <- function(all_results) {
  extract_top_features <- function(target_results, n = 50) {
    feature_counts <- list()
    for(tissue in names(target_results)) {
      if(!is.null(target_results[[tissue]]$feature_counts)) {
        counts <- target_results[[tissue]]$feature_counts
        for(feat in names(counts)) {
          if(is.null(feature_counts[[feat]])) feature_counts[[feat]] <- 0
          feature_counts[[feat]] <- feature_counts[[feat]] + counts[[feat]]
        }
      }
    }
    
    sorted_features <- sort(unlist(feature_counts), decreasing = TRUE)
    return(names(sorted_features)[1:min(n, length(sorted_features))])
  }
  
  rin_top <- extract_top_features(all_results[["SMRIN"]])
  auto_top <- extract_top_features(all_results[["SMATSSCR"]])
  
  overlap_count <- length(intersect(rin_top, auto_top))
  rin_unique <- length(setdiff(rin_top, auto_top))
  auto_unique <- length(setdiff(auto_top, rin_top))
  
  return(list(common = overlap_count, rin_unique = rin_unique, auto_unique = auto_unique))
}

figure6_plot <- create_feature_consistency_analysis(all_results)
overlap_stats <- calculate_feature_overlap_stats(all_results)

ggsave(file.path(output_path, "Figure6_Feature_Consistency.pdf"), 
       figure6_plot, width = 18, height = 14, dpi = 600, bg = "white")
```

#------------------------------------------------------------------------------
# 11. Figure 7: Model Residual Analysis
#------------------------------------------------------------------------------
```{r}
create_comprehensive_residual_analysis <- function(all_results, top_n = 6) {
  
  identify_top_tissues <- function(target_results, n) {
    performance_scores <- sapply(target_results, function(x) {
      if(is.null(x)) return(0)
      return(abs(x$performance$pooled_r))
    })
    top_tissues <- names(sort(performance_scores, decreasing = TRUE))[1:n]
    return(top_tissues[!is.na(top_tissues)])
  }
  
  top_rin_tissues <- identify_top_tissues(all_results[["SMRIN"]], top_n)
  top_auto_tissues <- identify_top_tissues(all_results[["SMATSSCR"]], top_n)
  
  create_residual_panel <- function(tissues, target, color, panel_title) {
    residual_plots <- list()
    
    for(i in 1:min(3, length(tissues))) {
      tissue_name <- tissues[i]
      result <- all_results[[target]][[tissue_name]]
      
      if(!is.null(result) && !is.null(result$all_predictions)) {
        predictions <- result$all_predictions
        predictions$residual <- predictions$predicted - predictions$actual
        r_value <- result$performance$pooled_r
        
        residual_plot <- ggplot(predictions, aes(x = actual, y = residual)) +
          geom_point(alpha = 0.7, color = color, size = 2) +
          geom_hline(yintercept = 0, linetype = "dashed", color = "red", size = 1) +
          geom_smooth(method = "loess", se = TRUE, color = "darkblue", alpha = 0.3) +
          theme_minimal() +
          labs(
            title = paste(tissue_name, "(R =", round(r_value, 3), ")"),
            x = if(i == 3) paste("Actual", ifelse(target == "SMRIN", "RIN", "Autolysis")) else "",
            y = if(i == 1) "Residual" else ""
          ) +
          theme(
            plot.title = element_text(size = 12, face = "bold"),
            axis.title = element_text(size = 10, face = "bold")
          )
        
        residual_plots[[i]] <- residual_plot
      }
    }
    
    panel <- grid.arrange(grobs = residual_plots, nrow = 1,
                         top = textGrob(panel_title, gp = gpar(fontsize = 16, fontface = "bold")))
    return(panel)
  }
  
  panel_a <- create_residual_panel(top_rin_tissues, "SMRIN", "#66CDAA", "A. RIN Model Residuals")
  panel_b <- create_residual_panel(top_auto_tissues, "SMATSSCR", "#FC8D62", "B. Autolysis Model Residuals")
  
  create_residual_distribution <- function() {
    all_residuals <- data.frame()
    
    for(target in c("SMRIN", "SMATSSCR")) {
      for(tissue in names(all_results[[target]])) {
        result <- all_results[[target]][[tissue]]
        if(!is.null(result) && !is.null(result$all_predictions)) {
          predictions <- result$all_predictions
          residual_data <- data.frame(
            tissue = tissue,
            target = ifelse(target == "SMRIN", "RIN", "Autolysis"),
            residual = predictions$predicted - predictions$actual,
            abs_residual = abs(predictions$predicted - predictions$actual)
          )
          all_residuals <- rbind(all_residuals, residual_data)
        }
      }
    }
    
    distribution_plot <- ggplot(all_residuals, aes(x = abs_residual, fill = target)) +
      geom_histogram(alpha = 0.75, bins = 30, position = "identity") +
      geom_vline(data = all_residuals %>% group_by(target) %>% summarise(mean_res = mean(abs_residual)),
                 aes(xintercept = mean_res, color = target), linetype = "dashed", size = 1.2) +
      scale_fill_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
      scale_color_manual(values = c("RIN" = "#2E8B57", "Autolysis" = "#D2691E")) +
      theme_minimal() +
      labs(
        title = "C. Distribution of Absolute Residuals",
        x = "Absolute Residual Magnitude",
        y = "Frequency",
        fill = "Model Type",
        subtitle = "Comparison of prediction errors across all tissue models"
      ) +
      theme(
        plot.title = element_text(size = 16, face = "bold"),
        plot.subtitle = element_text(size = 12),
        legend.position = "top",
        axis.title = element_text(size = 11, face = "bold")
      )
    
    return(distribution_plot)
  }
  
  create_performance_residual_relationship <- function() {
    performance_residual_data <- data.frame()
    
    for(target in c("SMRIN", "SMATSSCR")) {
      for(tissue in names(all_results[[target]])) {
        result <- all_results[[target]][[tissue]]
        if(!is.null(result) && !is.null(result$all_predictions)) {
          predictions <- result$all_predictions
          
          perf_data <- data.frame(
            tissue = tissue,
            target = ifelse(target == "SMRIN", "RIN", "Autolysis"),
            r_value = abs(result$performance$pooled_r),
            rmse = result$performance$pooled_rmse,
            mean_abs_residual = mean(abs(predictions$predicted - predictions$actual))
          )
          performance_residual_data <- rbind(performance_residual_data, perf_data)
        }
      }
    }
    
    relationship_plot <- ggplot(performance_residual_data, aes(x = r_value, y = mean_abs_residual, color = target)) +
      geom_point(size = 3.5, alpha = 0.8) +
      geom_smooth(method = "lm", se = TRUE, alpha = 0.3, size = 1.2) +
      scale_color_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
      theme_minimal() +
      labs(
        title = "D. Model Performance vs Residual Magnitude",
        x = "Correlation Coefficient |R|",
        y = "Mean Absolute Residual",
        color = "Model Type",
        subtitle = "Relationship between prediction accuracy and error magnitude"
      ) +
      theme(
        plot.title = element_text(size = 16, face = "bold"),
        plot.subtitle = element_text(size = 12),
        legend.position = "none",
        axis.title = element_text(size = 11, face = "bold")
      )
    
    return(relationship_plot)
  }
  
  panel_c <- create_residual_distribution()
  panel_d <- create_performance_residual_relationship()
  
  bottom_panels <- grid.arrange(panel_c, panel_d, ncol = 2)
  figure7_combined <- grid.arrange(panel_a, panel_b, bottom_panels, nrow = 3, heights = c(1, 1, 1))
  
  return(figure7_combined)
}

analyze_residual_patterns <- function(all_results) {
  residual_stats <- data.frame()
  
  for(target in c("SMRIN", "SMATSSCR")) {
    for(tissue in names(all_results[[target]])) {
      result <- all_results[[target]][[tissue]]
      if(!is.null(result) && !is.null(result$all_predictions)) {
        predictions <- result$all_predictions
        residuals <- predictions$predicted - predictions$actual
        
        stats_row <- data.frame(
          tissue = tissue,
          target = target,
          mean_residual = mean(residuals),
          sd_residual = sd(residuals),
          skewness = e1071::skewness(residuals),
          normality_p = shapiro.test(sample(residuals, min(5000, length(residuals))))$p.value
        )
        residual_stats <- rbind(residual_stats, stats_row)
      }
    }
  }
  
  return(residual_stats)
}

figure7_plot <- create_comprehensive_residual_analysis(all_results)
residual_statistics <- analyze_residual_patterns(all_results)

ggsave(file.path(output_path, "Figure7_Residual_Analysis.pdf"), 
       figure7_plot, width = 16, height = 14, dpi = 600, bg = "white")
```

#------------------------------------------------------------------------------
# 11. Figure 8: Biological Feature Interpretation
#------------------------------------------------------------------------------
```{r}
create_biological_interpretation_analysis <- function(all_results, merged_gtex) {
  
  analyze_feature_categories <- function() {
    extract_important_features <- function(target_results, n = 50) {
      feature_importance <- c()
      for(tissue in names(target_results)) {
        if(!is.null(target_results[[tissue]]$feature_counts)) {
          features <- names(target_results[[tissue]]$feature_counts)
          counts <- as.numeric(target_results[[tissue]]$feature_counts)
          feature_importance <- c(feature_importance, rep(features, counts))
        }
      }
      
      importance_table <- sort(table(feature_importance), decreasing = TRUE)
      return(names(importance_table)[1:min(n, length(importance_table))])
    }
    
    rin_important <- extract_important_features(all_results[["SMRIN"]])
    auto_important <- extract_important_features(all_results[["SMATSSCR"]])
    
    categorize_features <- function(feature_names) {
      feature_numbers <- as.numeric(sub("feature_", "", feature_names))
      categories <- case_when(
        feature_numbers <= 1024 ~ "Mean",
        feature_numbers <= 2048 ~ "Standard Deviation",
        feature_numbers <= 3072 ~ "Minimum",
        TRUE ~ "Maximum"
      )
      return(categories)
    }
    
    rin_categories <- categorize_features(rin_important)
    auto_categories <- categorize_features(auto_important)
    
    category_summary <- data.frame(
      Category = rep(c("Mean", "Standard Deviation", "Minimum", "Maximum"), 2),
      Count = c(table(rin_categories)[c("Mean", "Standard Deviation", "Minimum", "Maximum")],
               table(auto_categories)[c("Mean", "Standard Deviation", "Minimum", "Maximum")]),
      Model = rep(c("RIN", "Autolysis"), each = 4)
    )
    
    category_summary$Count[is.na(category_summary$Count)] <- 0
    
    category_plot <- ggplot(category_summary, aes(x = Category, y = Count, fill = Model)) +
      geom_bar(stat = "identity", position = "dodge", alpha = 0.85, width = 0.7) +
      geom_text(aes(label = Count), position = position_dodge(width = 0.7), 
                vjust = -0.3, size = 4, fontface = "bold") +
      scale_fill_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
      theme_minimal() +
      labs(
        title = "A. Feature Category Distribution",
        x = "Statistical Property Category",
        y = "Number of Important Features",
        subtitle = "Distribution of key features across statistical measures"
      ) +
      theme(
        plot.title = element_text(size = 16, face = "bold"),
        plot.subtitle = element_text(size = 12),
        legend.position = "top",
        axis.title = element_text(size = 12, face = "bold"),
        axis.text.x = element_text(size = 11, face = "bold")
      )
    
    return(category_plot)
  }
  
  create_tissue_variability_analysis <- function() {
    feature_cols <- grep("^feature_", names(merged_gtex), value = TRUE)
    
    get_feature_importance <- function(target_results) {
      feature_scores <- list()
      for(tissue in names(target_results)) {
        if(!is.null(target_results[[tissue]]$feature_counts)) {
          counts <- target_results[[tissue]]$feature_counts
          for(feat in names(counts)) {
            if(is.null(feature_scores[[feat]])) feature_scores[[feat]] <- 0
            feature_scores[[feat]] <- feature_scores[[feat]] + counts[[feat]]
          }
        }
      }
      
      sorted_scores <- sort(unlist(feature_scores), decreasing = TRUE)
      return(names(sorted_scores)[1:min(25, length(sorted_scores))])
    }
    
    important_features <- get_feature_importance(all_results[["SMRIN"]])
    variability_data <- data.frame()
    
    for(feature in important_features) {
      if(feature %in% feature_cols) {
        tissue_statistics <- merged_gtex[, .(
          mean_value = mean(get(feature), na.rm = TRUE),
          sd_value = sd(get(feature), na.rm = TRUE)
        ), by = tissue_main]
        
        overall_variance <- var(tissue_statistics$mean_value, na.rm = TRUE)
        
        importance_score <- sum(sapply(all_results[["SMRIN"]], function(x) {
          if(!is.null(x$feature_counts) && feature %in% names(x$feature_counts)) {
            return(x$feature_counts[[feature]])
          }
          return(0)
        }))
        
        feature_num <- as.numeric(sub("feature_", "", feature))
        category <- case_when(
          feature_num <= 1024 ~ "Mean",
          feature_num <= 2048 ~ "Std Dev",
          feature_num <= 3072 ~ "Minimum",
          TRUE ~ "Maximum"
        )
        
        variability_data <- rbind(variability_data, data.frame(
          feature = feature,
          tissue_variance = overall_variance,
          importance_score = importance_score,
          category = category
        ))
      }
    }
    
    variability_plot <- ggplot(variability_data, aes(x = tissue_variance, y = importance_score, color = category)) +
      geom_point(size = 4, alpha = 0.8) +
      geom_smooth(method = "lm", se = TRUE, alpha = 0.3, color = "darkblue") +
      scale_color_brewer(palette = "Set2", name = "Feature\nCategory") +
      theme_minimal() +
      labs(
        title = "B. Feature Importance vs Tissue Variability",
        x = "Variance Across Tissue Types",
        y = "Importance Score (Selection Frequency)",
        subtitle = "Relationship between tissue discrimination and feature importance"
      ) +
      theme(
        plot.title = element_text(size = 16, face = "bold"),
        plot.subtitle = element_text(size = 12),
        legend.position = "right",
        axis.title = element_text(size = 12, face = "bold")
      )
    
    return(variability_plot)
  }
  
  create_feature_correlation_matrix <- function() {
    extract_consensus_features <- function(target_results, n = 20) {
      feature_counts <- list()
      for(tissue in names(target_results)) {
        if(!is.null(target_results[[tissue]]$feature_counts)) {
          counts <- target_results[[tissue]]$feature_counts
          for(feat in names(counts)) {
            if(is.null(feature_counts[[feat]])) feature_counts[[feat]] <- 0
            feature_counts[[feat]] <- feature_counts[[feat]] + counts[[feat]]
          }
        }
      }
      
      sorted_features <- sort(unlist(feature_counts), decreasing = TRUE)
      return(names(sorted_features)[1:min(n, length(sorted_features))])
    }
    
    consensus_features <- extract_consensus_features(all_results[["SMRIN"]], 15)
    
    if(length(consensus_features) > 1) {
      feature_matrix <- as.matrix(merged_gtex[, ..consensus_features])
      feature_matrix <- feature_matrix[complete.cases(feature_matrix), ]
      
      if(nrow(feature_matrix) > 10) {
        correlation_matrix <- cor(feature_matrix, use = "complete.obs")
        
        cor_long <- reshape2::melt(correlation_matrix)
        cor_long$Var1 <- sub("feature_", "", cor_long$Var1)
        cor_long$Var2 <- sub("feature_", "", cor_long$Var2)
        
        correlation_plot <- ggplot(cor_long, aes(x = Var1, y = Var2, fill = value)) +
          geom_tile(color = "white", size = 0.5) +
          scale_fill_gradient2(low = "#2166AC", mid = "white", high = "#D73027", 
                              midpoint = 0, name = "Correlation\nCoefficient",
                              limits = c(-1, 1)) +
          theme_minimal() +
          theme(
            axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
            axis.text.y = element_text(size = 10),
            plot.title = element_text(size = 16, face = "bold"),
            plot.subtitle = element_text(size = 12),
            legend.position = "right"
          ) +
          labs(
            title = "C. Feature Correlation Matrix",
            x = "Feature ID",
            y = "Feature ID",
            subtitle = "Correlation structure among top predictive features"
          ) +
          coord_fixed()
        
        return(correlation_plot)
      }
    }
    
    empty_plot <- ggplot() + 
      annotate("text", x = 0.5, y = 0.5, 
               label = "Insufficient data\nfor correlation analysis", 
               size = 6, hjust = 0.5) +
      theme_void() +
      labs(title = "C. Feature Correlation Matrix") +
      theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5))
    
    return(empty_plot)
  }
  
  panel_a <- analyze_feature_categories()
  panel_b <- create_tissue_variability_analysis()
  panel_c <- create_feature_correlation_matrix()
  
  top_panels <- grid.arrange(panel_a, panel_b, ncol = 2)
  figure8_combined <- grid.arrange(top_panels, panel_c, nrow = 2, heights = c(1, 1))
  
  return(figure8_combined)
}

analyze_biological_significance <- function(all_results) {
  for(target in c("SMRIN", "SMATSSCR")) {
    all_features <- unlist(lapply(all_results[[target]], function(x) {
      if(!is.null(x$feature_counts)) names(x$feature_counts) else NULL
    }))
    
    feature_nums <- as.numeric(sub("feature_", "", all_features))
    mean_features <- sum(feature_nums <= 1024)
    std_features <- sum(feature_nums > 1024 & feature_nums <= 2048)
    min_features <- sum(feature_nums > 2048 & feature_nums <= 3072)
    max_features <- sum(feature_nums > 3072)
  }
}

figure8_plot <- create_biological_interpretation_analysis(all_results, merged_gtex)
analyze_biological_significance(all_results)

ggsave(file.path(output_path, "Figure8_Biological_Interpretation.pdf"), 
       figure8_plot, width = 16, height = 14, dpi = 600, bg = "white")
```

#------------------------------------------------------------------------------
# 12. Supplementary Figure S1: Quality Score Distributions by Tissue
#------------------------------------------------------------------------------
```{r}
create_quality_score_distributions <- function(merged_gtex) {
  
  prepare_quality_data <- function() {
    rin_data <- merged_gtex[!is.na(SMRIN), .(tissue_main, SMRIN)]
    setnames(rin_data, "SMRIN", "Score")
    rin_data$Score_Type <- "RIN Score"
    
    auto_data <- merged_gtex[!is.na(SMATSSCR), .(tissue_main, SMATSSCR)]
    setnames(auto_data, "SMATSSCR", "Score")
    auto_data$Score_Type <- "Autolysis Score"
    
    return(list(rin = rin_data, autolysis = auto_data))
  }
  
  get_top_tissues <- function(n_tissues = 20) {
    tissue_counts <- merged_gtex[, .N, by = tissue_main][order(-N)]
    return(head(tissue_counts$tissue_main, n_tissues))
  }
  
  quality_data <- prepare_quality_data()
  top_tissues <- get_top_tissues()
  
  rin_filtered <- quality_data$rin[tissue_main %in% top_tissues]
  auto_filtered <- quality_data$autolysis[tissue_main %in% top_tissues]
  
  rin_filtered$tissue_main <- factor(rin_filtered$tissue_main, levels = top_tissues)
  auto_filtered$tissue_main <- factor(auto_filtered$tissue_main, levels = top_tissues)
  
  create_rin_distribution_panel <- function() {
    rin_violin <- ggplot(rin_filtered, aes(x = tissue_main, y = Score)) +
      geom_violin(fill = "#66CDAA", alpha = 0.8, trim = FALSE, scale = "width") +
      geom_boxplot(width = 0.15, alpha = 0.9, outlier.size = 1, outlier.alpha = 0.6) +
      stat_summary(fun = median, geom = "point", color = "darkblue", size = 1.5) +
      theme_minimal() +
      theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 11, face = "bold"),
        plot.title = element_text(size = 18, face = "bold"),
        plot.subtitle = element_text(size = 12),
        axis.title = element_text(size = 12, face = "bold")
      ) +
      labs(
        title = "A. RIN Score Distributions by Tissue Type",
        subtitle = "RNA Integrity Number across major tissue types",
        x = NULL,
        y = "RIN Score"
      ) +
      scale_y_continuous(limits = c(1, 10), breaks = seq(2, 10, 2))
    
    return(rin_violin)
  }
  
  create_autolysis_distribution_panel <- function() {
    auto_violin <- ggplot(auto_filtered, aes(x = tissue_main, y = Score)) +
      geom_violin(fill = "#FC8D62", alpha = 0.8, trim = FALSE, scale = "width") +
      geom_boxplot(width = 0.15, alpha = 0.9, outlier.size = 1, outlier.alpha = 0.6) +
      stat_summary(fun = median, geom = "point", color = "darkred", size = 1.5) +
      theme_minimal() +
      theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 11, face = "bold"),
        plot.title = element_text(size = 18, face = "bold"),
        plot.subtitle = element_text(size = 12),
        axis.title = element_text(size = 12, face = "bold")
      ) +
      labs(
        title = "B. Autolysis Score Distributions by Tissue Type",
        subtitle = "Tissue degradation levels across major tissue types",
        x = "Tissue Type",
        y = "Autolysis Score"
      ) +
      scale_y_continuous(limits = c(0, 3), breaks = 0:3,
                        labels = c("0 (None)", "1 (Mild)", "2 (Moderate)", "3 (Severe)"))
    
    return(auto_violin)
  }
  
  calculate_distribution_stats <- function() {
    rin_stats <- rin_filtered[, .(
      mean_score = round(mean(Score, na.rm = TRUE), 2),
      median_score = round(median(Score, na.rm = TRUE), 2),
      sd_score = round(sd(Score, na.rm = TRUE), 2),
      sample_size = .N
    ), by = tissue_main][order(-mean_score)]
    
    auto_stats <- auto_filtered[, .(
      mean_score = round(mean(Score, na.rm = TRUE), 2),
      median_score = round(median(Score, na.rm = TRUE), 2),
      sd_score = round(sd(Score, na.rm = TRUE), 2),
      sample_size = .N
    ), by = tissue_main][order(mean_score)]
    
    return(list(rin_stats = rin_stats, auto_stats = auto_stats))
  }
  
  panel_a <- create_rin_distribution_panel()
  panel_b <- create_autolysis_distribution_panel()
  distribution_stats <- calculate_distribution_stats()
  
  supp_fig_s1 <- grid.arrange(panel_a, panel_b, nrow = 2)
  
  return(supp_fig_s1)
}

analyze_quality_correlations <- function(merged_gtex) {
  quality_subset <- merged_gtex[!is.na(SMRIN) & !is.na(SMATSSCR)]
  
  if(nrow(quality_subset) > 10) {
    correlation_test <- cor.test(quality_subset$SMRIN, quality_subset$SMATSSCR)
    return(correlation_test)
  } else {
    return(NULL)
  }
}

supp_figure_s1 <- create_quality_score_distributions(merged_gtex)
quality_correlations <- analyze_quality_correlations(merged_gtex)

ggsave(file.path(output_path, "SuppFig_S1_Quality_Distributions.pdf"), 
       supp_figure_s1, width = 18, height = 12, dpi = 600, bg = "white")
```

#------------------------------------------------------------------------------
# 13. Supplementary Figure S2: Cross-validation Stability Analysis
#------------------------------------------------------------------------------
```{r}
create_cv_stability_analysis <- function(all_results) {
  
  extract_fold_performance <- function(target_results) {
    fold_performance <- data.frame()
    
    for(tissue in names(target_results)) {
      result <- target_results[[tissue]]
      if(!is.null(result) && !is.null(result$fold_results)) {
        
        for(fold_result in result$fold_results) {
          fold_data <- data.frame(
            tissue = tissue,
            fold = fold_result$fold,
            r_value = abs(fold_result$r),
            rmse = fold_result$rmse,
            mae = fold_result$mae
          )
          fold_performance <- rbind(fold_performance, fold_data)
        }
      }
    }
    
    return(fold_performance)
  }
  
  rin_fold_data <- extract_fold_performance(all_results[["SMRIN"]])
  auto_fold_data <- extract_fold_performance(all_results[["SMATSSCR"]])
  
  rin_fold_data$target <- "RIN"
  auto_fold_data$target <- "Autolysis"
  combined_fold_data <- rbind(rin_fold_data, auto_fold_data)
  
  create_stability_scatter <- function() {
    stability_metrics <- combined_fold_data %>%
      group_by(tissue, target) %>%
      summarise(
        mean_r = mean(r_value, na.rm = TRUE),
        sd_r = sd(r_value, na.rm = TRUE),
        cv_r = sd(r_value, na.rm = TRUE) / mean(r_value, na.rm = TRUE),
        .groups = 'drop'
      ) %>%
      filter(is.finite(cv_r) & cv_r > 0)
    
    stability_plot <- ggplot(stability_metrics, aes(x = mean_r, y = cv_r, color = target)) +
      geom_point(size = 3.5, alpha = 0.8) +
      geom_smooth(method = "lm", se = TRUE, alpha = 0.3) +
      scale_color_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
      theme_minimal() +
      labs(
        title = "A. Model Stability vs Performance",
        x = "Mean |R| Across Folds",
        y = "Coefficient of Variation (|R|)",
        color = "Model Type",
        subtitle = "Lower CV indicates more stable cross-validation performance"
      ) +
      theme(
        plot.title = element_text(size = 16, face = "bold"),
        plot.subtitle = element_text(size = 12),
        legend.position = "top",
        axis.title = element_text(size = 12, face = "bold")
      )
    
    return(stability_plot)
  }
  
  create_fold_variability_boxplots <- function() {
    top_tissues_rin <- combined_fold_data %>% 
      filter(target == "RIN") %>%
      group_by(tissue) %>%
      summarise(mean_r = mean(r_value, na.rm = TRUE), .groups = 'drop') %>%
      arrange(desc(mean_r)) %>% 
      slice(1:8) %>% 
      pull(tissue)
    
    top_tissues_auto <- combined_fold_data %>% 
      filter(target == "Autolysis") %>%
      group_by(tissue) %>%
      summarise(mean_r = mean(r_value, na.rm = TRUE), .groups = 'drop') %>%
      arrange(desc(mean_r)) %>% 
      slice(1:8) %>% 
      pull(tissue)
    
    fold_subset <- combined_fold_data %>%
      filter((target == "RIN" & tissue %in% top_tissues_rin) |
             (target == "Autolysis" & tissue %in% top_tissues_auto))
    
    variability_plot <- ggplot(fold_subset, aes(x = tissue, y = r_value, fill = target)) +
      geom_boxplot(alpha = 0.8, outlier.alpha = 0.6) +
      facet_wrap(~target, scales = "free_x", ncol = 1) +
      scale_fill_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
      theme_minimal() +
      theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
        legend.position = "none",
        strip.text = element_text(size = 14, face = "bold"),
        plot.title = element_text(size = 16, face = "bold"),
        axis.title = element_text(size = 12, face = "bold")
      ) +
      labs(
        title = "B. Cross-Fold Performance Variability",
        x = "Tissue Type",
        y = "|R| Value Across Folds",
        subtitle = "Box plots show distribution of performance across CV folds"
      )
    
    return(variability_plot)
  }
  
  create_stability_ranking <- function() {
    stability_ranking <- combined_fold_data %>%
      group_by(tissue, target) %>%
      summarise(
        mean_r = mean(r_value, na.rm = TRUE),
        cv_r = sd(r_value, na.rm = TRUE) / mean(r_value, na.rm = TRUE),
        .groups = 'drop'
      ) %>%
      filter(is.finite(cv_r) & cv_r > 0) %>%
      arrange(cv_r) %>%
      mutate(
        stability_rank = row_number(),
        stability_category = cut(cv_r, 
                               breaks = c(0, 0.1, 0.2, 0.5, Inf),
                               labels = c("Very Stable", "Stable", "Moderate", "Unstable"))
      )
    
    ranking_plot <- ggplot(stability_ranking, aes(x = stability_rank, y = cv_r, color = target)) +
      geom_point(size = 3, alpha = 0.8) +
      geom_hline(yintercept = c(0.1, 0.2, 0.5), linetype = "dashed", alpha = 0.6) +
      scale_color_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
      theme_minimal() +
      labs(
        title = "C. Model Stability Ranking",
        x = "Stability Rank (1 = Most Stable)",
        y = "Coefficient of Variation (|R|)",
        color = "Model Type",
        subtitle = "Horizontal lines indicate stability thresholds"
      ) +
      theme(
        plot.title = element_text(size = 16, face = "bold"),
        plot.subtitle = element_text(size = 12),
        legend.position = "none",
        axis.title = element_text(size = 12, face = "bold")
      )
    
    return(ranking_plot)
  }
  
  panel_a <- create_stability_scatter()
  panel_b <- create_fold_variability_boxplots()
  panel_c <- create_stability_ranking()
  
  top_panels <- grid.arrange(panel_a, panel_c, ncol = 2)
  supp_fig_s2 <- grid.arrange(top_panels, panel_b, nrow = 2, heights = c(1, 1.5))
  
  return(supp_fig_s2)
}

calculate_stability_stats <- function(all_results) {
  stability_summary <- data.frame()
  
  for(target in c("SMRIN", "SMATSSCR")) {
    for(tissue in names(all_results[[target]])) {
      result <- all_results[[target]][[tissue]]
      if(!is.null(result) && !is.null(result$fold_results)) {
        
        fold_r_values <- sapply(result$fold_results, function(x) abs(x$r))
        
        stability_row <- data.frame(
          tissue = tissue,
          target = target,
          mean_r = mean(fold_r_values),
          cv_r = sd(fold_r_values) / mean(fold_r_values),
          min_r = min(fold_r_values),
          max_r = max(fold_r_values)
        )
        stability_summary <- rbind(stability_summary, stability_row)
      }
    }
  }
  
  by_target <- stability_summary %>%
    group_by(target) %>%
    summarise(
      mean_cv = round(mean(cv_r, na.rm = TRUE), 3),
      median_cv = round(median(cv_r, na.rm = TRUE), 3),
      stable_models = sum(cv_r < 0.2, na.rm = TRUE),
      total_models = n(),
      .groups = 'drop'
    )
  
  return(stability_summary)
}

supp_figure_s2 <- create_cv_stability_analysis(all_results)
stability_stats <- calculate_stability_stats(all_results)

ggsave(file.path(output_path, "SuppFig_S2_CV_Stability.pdf"), 
       supp_figure_s2, width = 14, height = 12, dpi = 600, bg = "white")
```


#------------------------------------------------------------------------------
# 14. Supplementary Figure S3: Extended Comparative Analysis
#------------------------------------------------------------------------------
```{r}
create_extended_comparative_analysis <- function(all_results, rin_summary, autolysis_summary) {
  
  create_cross_model_correlation <- function() {
    common_tissues <- intersect(rin_summary$Tissue, autolysis_summary$Tissue)
    
    comparison_data <- data.frame(
      Tissue = common_tissues,
      RIN_R = rin_summary$R[match(common_tissues, rin_summary$Tissue)],
      Auto_R = autolysis_summary$R[match(common_tissues, autolysis_summary$Tissue)],
      RIN_RMSE = rin_summary$RMSE[match(common_tissues, rin_summary$Tissue)],
      Auto_RMSE = autolysis_summary$RMSE[match(common_tissues, autolysis_summary$Tissue)]
    )
    
    r_correlation <- cor(abs(comparison_data$RIN_R), abs(comparison_data$Auto_R), use = "complete.obs")
    
    correlation_plot <- ggplot(comparison_data, aes(x = abs(RIN_R), y = abs(Auto_R))) +
      geom_point(size = 3.5, alpha = 0.8, color = "darkblue") +
      geom_smooth(method = "lm", se = TRUE, color = "red", alpha = 0.3) +
      geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "gray", size = 1) +
      theme_minimal() +
      labs(
        title = "A. Cross-Model Performance Correlation",
        x = "RIN Model |R|",
        y = "Autolysis Model |R|",
        subtitle = paste("Correlation =", round(r_correlation, 3))
      ) +
      theme(
        plot.title = element_text(size = 16, face = "bold"),
        plot.subtitle = element_text(size = 12),
        axis.title = element_text(size = 12, face = "bold")
      ) +
      coord_fixed()
    
    return(correlation_plot)
  }
  
  create_sample_size_performance <- function() {
    combined_data <- rbind(
      data.frame(rin_summary[, c("Tissue", "Samples", "R")], Model = "RIN"),
      data.frame(autolysis_summary[, c("Tissue", "Samples", "R")], Model = "Autolysis")
    )
    
    combined_data$R_abs <- abs(combined_data$R)
    combined_data$Size_Bin <- cut(combined_data$Samples, 
                                 breaks = c(0, 50, 100, 200, 500, Inf),
                                 labels = c("≤50", "51-100", "101-200", "201-500", ">500"))
    
    size_performance_plot <- ggplot(combined_data, aes(x = Size_Bin, y = R_abs, fill = Model)) +
      geom_boxplot(alpha = 0.8, outlier.alpha = 0.6) +
      scale_fill_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
      theme_minimal() +
      labs(
        title = "B. Model Performance by Sample Size Category",
        x = "Sample Size Category",
        y = "Absolute Correlation Coefficient |R|",
        subtitle = "Performance distribution across sample size bins"
      ) +
      theme(
        plot.title = element_text(size = 16, face = "bold"),
        plot.subtitle = element_text(size = 12),
        legend.position = "top",
        axis.title = element_text(size = 12, face = "bold")
      )
    
    return(size_performance_plot)
  }
  
  create_tissue_preference_analysis <- function() {
    common_tissues <- intersect(rin_summary$Tissue, autolysis_summary$Tissue)
    
    preference_data <- data.frame(
      Tissue = common_tissues,
      RIN_R = abs(rin_summary$R[match(common_tissues, rin_summary$Tissue)]),
      Auto_R = abs(autolysis_summary$R[match(common_tissues, autolysis_summary$Tissue)])
    )
    
    preference_data$Better_Model <- ifelse(
      preference_data$RIN_R > preference_data$Auto_R, "RIN", "Autolysis"
    )
    preference_data$Performance_Difference <- abs(preference_data$RIN_R - preference_data$Auto_R)
    preference_data$Max_R <- pmax(preference_data$RIN_R, preference_data$Auto_R)
    
    preference_data$Tissue_System <- case_when(
      grepl("Brain|Nerve|Spinal", preference_data$Tissue) ~ "Nervous",
      grepl("Heart|Artery|Aorta", preference_data$Tissue) ~ "Cardiovascular", 
      grepl("Lung|Trachea", preference_data$Tissue) ~ "Respiratory",
      grepl("Liver|Pancreas|Stomach|Colon|Small|Esophagus", preference_data$Tissue) ~ "Digestive",
      grepl("Kidney|Bladder", preference_data$Tissue) ~ "Urinary",
      grepl("Muscle|Skin", preference_data$Tissue) ~ "Musculoskeletal",
      grepl("Thyroid|Adrenal|Pituitary", preference_data$Tissue) ~ "Endocrine",
      TRUE ~ "Other"
    )
    
    preference_plot <- ggplot(preference_data, aes(x = Max_R, y = Performance_Difference, 
                                                  color = Better_Model, shape = Tissue_System)) +
      geom_point(size = 4, alpha = 0.8) +
      scale_color_manual(values = c("RIN" = "#66CDAA", "Autolysis" = "#FC8D62")) +
      scale_shape_manual(values = c(16, 17, 18, 15, 3, 4, 8, 1)[1:length(unique(preference_data$Tissue_System))]) +
      theme_minimal() +
      labs(
        title = "C. Tissue Categories and Model Preference",
        x = "Maximum |R| Value",
        y = "Absolute Difference in |R|",
        color = "Better Model",
        shape = "Tissue System",
        subtitle = "Model preference patterns across biological systems"
      ) +
      theme(
        plot.title = element_text(size = 16, face = "bold"),
        plot.subtitle = element_text(size = 12),
        legend.position = "right",
        axis.title = element_text(size = 12, face = "bold")
      )
    
    return(preference_plot)
  }
  
  create_feature_overlap_analysis <- function() {
    extract_features <- function(target_results, threshold = 5) {
      feature_counts <- list()
      for(tissue in names(target_results)) {
        if(!is.null(target_results[[tissue]]$feature_counts)) {
          counts <- target_results[[tissue]]$feature_counts
          for(feat in names(counts)) {
            if(is.null(feature_counts[[feat]])) feature_counts[[feat]] <- 0
            feature_counts[[feat]] <- feature_counts[[feat]] + counts[[feat]]
          }
        }
      }
      
      important_features <- names(feature_counts)[feature_counts >= threshold]
      return(important_features)
    }
    
    thresholds <- c(3, 5, 10, 15, 20)
    overlap_data <- data.frame()
    
    for(thresh in thresholds) {
      rin_thresh <- extract_features(all_results[["SMRIN"]], thresh)
      auto_thresh <- extract_features(all_results[["SMATSSCR"]], thresh)
      
      overlap_count <- length(intersect(rin_thresh, auto_thresh))
      rin_only <- length(setdiff(rin_thresh, auto_thresh))
      auto_only <- length(setdiff(auto_thresh, rin_thresh))
      
      overlap_data <- rbind(overlap_data, data.frame(
        Threshold = thresh,
        Overlap = overlap_count,
        RIN_Only = rin_only,
        Auto_Only = auto_only
      ))
    }
    
    overlap_long <- reshape2::melt(overlap_data, id.vars = "Threshold", 
                                  variable.name = "Category", value.name = "Count")
    
    overlap_plot <- ggplot(overlap_long, aes(x = factor(Threshold), y = Count, fill = Category)) +
      geom_bar(stat = "identity", position = "stack") +
      scale_fill_manual(values = c("Overlap" = "#8E44AD", "RIN_Only" = "#66CDAA", "Auto_Only" = "#FC8D62"),
                       labels = c("Common Features", "RIN Only", "Autolysis Only")) +
      theme_minimal() +
      labs(
        title = "D. Feature Overlap at Different Selection Thresholds",
        x = "Minimum Selection Frequency",
        y = "Number of Features",
        fill = "Feature Type",
        subtitle = "Overlap analysis across selection frequency thresholds"
      ) +
      theme(
        plot.title = element_text(size = 16, face = "bold"),
        plot.subtitle = element_text(size = 12),
        legend.position = "top",
        axis.title = element_text(size = 12, face = "bold")
      )
    
    return(overlap_plot)
  }
  
  panel_a <- create_cross_model_correlation()
  panel_b <- create_sample_size_performance()
  panel_c <- create_tissue_preference_analysis()
  panel_d <- create_feature_overlap_analysis()
  
  combined_plot <- grid.arrange(panel_a, panel_b, panel_c, panel_d, nrow = 2, ncol = 2)
  
  return(combined_plot)
}

create_model_comparison_summary <- function(rin_summary, autolysis_summary) {
  common_tissues <- intersect(rin_summary$Tissue, autolysis_summary$Tissue)
  
  comparison_summary <- data.frame(
    Tissue = common_tissues,
    RIN_R = abs(rin_summary$R[match(common_tissues, rin_summary$Tissue)]),
    Auto_R = abs(autolysis_summary$R[match(common_tissues, autolysis_summary$Tissue)])
  )
  
  comparison_summary$Better_Model <- ifelse(
    comparison_summary$RIN_R > comparison_summary$Auto_R, "RIN", "Autolysis"
  )
  
  model_preference <- table(comparison_summary$Better_Model)
  
  return(list(
    summary = comparison_summary,
    preference_counts = model_preference,
    mean_performance = c(
      RIN = mean(comparison_summary$RIN_R, na.rm = TRUE),
      Autolysis = mean(comparison_summary$Auto_R, na.rm = TRUE)
    )
  ))
}

supp_figure_s3 <- create_extended_comparative_analysis(all_results, rin_summary, autolysis_summary)
comparison_summary <- create_model_comparison_summary(rin_summary, autolysis_summary)

ggsave(file.path(output_path, "SuppFig_S3_Extended_Comparison.pdf"), 
       supp_figure_s3, width = 16, height = 14, dpi = 600, bg = "white")
```

#------------------------------------------------------------------------------
# 15. Comprehensive Final Report Generation
#------------------------------------------------------------------------------
```{r}
generate_comprehensive_report <- function() {
  report_path <- file.path(data_path, "processed/comprehensive_gtex_report.txt")
  
  sink(report_path)
  
  cat("================================================================================\n")
  cat("                    COMPREHENSIVE GTEx ANALYSIS REPORT                          \n")
  cat("================================================================================\n\n")
  
  # Header Information
  cat("ANALYSIS OVERVIEW\n")
  cat("-----------------\n")
  cat(paste("Report Generated:", Sys.time(), "\n"))
  cat(paste("Pipeline Version: GTEx Analysis Pipeline v1.0\n"))
  cat(paste("Analysis Duration:", format(Sys.time() - start_time, digits = 3), "\n\n"))
  
  # Dataset Summary
  cat("DATASET SUMMARY\n")
  cat("===============\n")
  cat(paste("Total Samples Analyzed:", nrow(merged_gtex), "\n"))
  cat(paste("Total Tissue Types:", length(unique(merged_gtex$tissue_main)), "\n"))
  cat(paste("Total Features Extracted:", length(grep("^feature_", names(merged_gtex))), "\n"))
  cat(paste("Male Subjects:", sum(merged_gtex$sex == "male"), "\n"))
  cat(paste("Female Subjects:", sum(merged_gtex$sex == "female"), "\n\n"))
  
  # Top tissue types by sample count
  tissue_counts <- merged_gtex[, .N, by = tissue_main][order(-N)]
  cat("Top 10 Tissue Types by Sample Count:\n")
  for(i in 1:min(10, nrow(tissue_counts))) {
    cat(sprintf("  %2d. %-25s: %4d samples\n", i, tissue_counts$tissue_main[i], tissue_counts$N[i]))
  }
  cat("\n")
  
  # Quality Metrics Analysis
  cat("QUALITY METRICS ANALYSIS\n")
  cat("========================\n")
  
  # RIN Analysis
  rin_data <- merged_gtex[!is.na(SMRIN)]
  if(nrow(rin_data) > 0) {
    cat("RIN Score Distribution:\n")
    cat(paste("  Samples with RIN data:", nrow(rin_data), "of", nrow(merged_gtex), 
              sprintf("(%.1f%%)\n", 100 * nrow(rin_data) / nrow(merged_gtex))))
    cat(paste("  Mean RIN Score:", round(mean(rin_data$SMRIN), 2), "\n"))
    cat(paste("  Median RIN Score:", round(median(rin_data$SMRIN), 2), "\n"))
    cat(paste("  RIN Range:", round(min(rin_data$SMRIN), 2), "-", round(max(rin_data$SMRIN), 2), "\n"))
    cat(paste("  High Quality (RIN ≥ 7.0):", sum(rin_data$SMRIN >= 7.0), 
              sprintf("(%.1f%%)\n", 100 * sum(rin_data$SMRIN >= 7.0) / nrow(rin_data))))
  }
  
  # Autolysis Analysis
  autolysis_data <- merged_gtex[!is.na(SMATSSCR)]
  if(nrow(autolysis_data) > 0) {
    cat("\nAutolysis Score Distribution:\n")
    cat(paste("  Samples with Autolysis data:", nrow(autolysis_data), "of", nrow(merged_gtex),
              sprintf("(%.1f%%)\n", 100 * nrow(autolysis_data) / nrow(merged_gtex))))
    cat(paste("  Mean Autolysis Score:", round(mean(autolysis_data$SMATSSCR), 2), "\n"))
    cat(paste("  Median Autolysis Score:", round(median(autolysis_data$SMATSSCR), 2), "\n"))
    autolysis_counts <- table(autolysis_data$SMATSSCR)
    for(score in names(autolysis_counts)) {
      cat(sprintf("  Score %s: %d samples (%.1f%%)\n", score, autolysis_counts[score],
                  100 * autolysis_counts[score] / nrow(autolysis_data)))
    }
    cat(paste("  High Quality (Score ≤ 2):", sum(autolysis_data$SMATSSCR <= 2),
              sprintf("(%.1f%%)\n", 100 * sum(autolysis_data$SMATSSCR <= 2) / nrow(autolysis_data))))
  }
  
  # Dimensionality Reduction Results
  cat("\nDIMENSIONALITY REDUCTION ANALYSIS\n")
  cat("=================================\n")
  
  if(exists("pca_results")) {
    cat("Principal Component Analysis:\n")
    cat(paste("  PC1 Variance Explained:", sprintf("%.1f%%", pca_results$variance_explained[1] * 100), "\n"))
    cat(paste("  PC2 Variance Explained:", sprintf("%.1f%%", pca_results$variance_explained[2] * 100), "\n"))
    cat(paste("  First 10 PCs Cumulative Variance:", sprintf("%.1f%%", pca_results$cumulative_variance[10] * 100), "\n"))
    cat(paste("  First 50 PCs Cumulative Variance:", sprintf("%.1f%%", pca_results$cumulative_variance[50] * 100), "\n"))
  }
  
  if(exists("tissue_sil")) {
    cat("\nUMAP Clustering Analysis:\n")
    cat(paste("  Overall Silhouette Score:", sprintf("%.3f", mean(sil[,3], na.rm = TRUE)), "\n"))
    cat("  Top 5 Best-Separated Tissues (by Silhouette Score):\n")
    top_silhouette <- head(tissue_sil[order(-avg_silhouette)], 5)
    for(i in 1:nrow(top_silhouette)) {
      cat(sprintf("    %d. %-25s: %.3f\n", i, top_silhouette$tissue[i], top_silhouette$avg_silhouette[i]))
    }
  }
  
  # Variance Analysis Results
  cat("\nVARIANCE ANALYSIS (ANOVA)\n")
  cat("=========================\n")
  
  if(exists("rin_variance")) {
    cat("RIN Score Variance Explained by Demographics:\n")
    rin_variance_sorted <- rin_variance[order(-rin_variance$var_explained),]
    for(i in 1:nrow(rin_variance_sorted)) {
      cat(sprintf("  %-20s: %5.1f%% (p %s)\n", 
                  rin_variance_sorted$label[i], 
                  rin_variance_sorted$var_explained[i],
                  rin_variance_sorted$significance[i]))
    }
  }
  
  if(exists("autolysis_variance")) {
    cat("\nAutolysis Score Variance Explained by Demographics:\n")
    autolysis_variance_sorted <- autolysis_variance[order(-autolysis_variance$var_explained),]
    for(i in 1:nrow(autolysis_variance_sorted)) {
      cat(sprintf("  %-20s: %5.1f%% (p %s)\n", 
                  autolysis_variance_sorted$label[i], 
                  autolysis_variance_sorted$var_explained[i],
                  autolysis_variance_sorted$significance[i]))
    }
  }
  
  # Correlation Analysis
  cat("\nCORRELATION ANALYSIS\n")
  cat("====================\n")
  
  if(exists("tissue_corr")) {
    cat("RIN vs Autolysis Correlations by Tissue:\n")
    tissue_corr_sorted <- tissue_corr[order(-abs(tissue_corr$correlation)),]
    cat("  Strongest Correlations (Top 10):\n")
    top_corr <- head(tissue_corr_sorted, 10)
    for(i in 1:nrow(top_corr)) {
      cat(sprintf("    %-25s: r = %6.3f (p %s, n = %d)\n", 
                  top_corr$tissue_main[i], 
                  top_corr$correlation[i],
                  top_corr$significance[i],
                  top_corr$n_samples[i]))
    }
  }
  
  # Machine Learning Model Results
  cat("\nMACHINE LEARNING MODEL PERFORMANCE\n")
  cat("==================================\n")
  
  if(exists("all_results")) {
    # RIN Models
    if("SMRIN" %in% names(all_results)) {
      rin_models <- all_results[["SMRIN"]]
      rin_r_values <- sapply(rin_models, function(x) {
        if(is.null(x)) return(NA)
        return(abs(x$performance$pooled_r))  # Use absolute R
        # return(sqrt(x$performance$pooled_r2)) # use when required for R²
      })
      rin_r_values <- rin_r_values[!is.na(rin_r_values)]
      
      cat("RIN Score Prediction Models:\n")
      cat(paste("  Successful models:", length(rin_r_values), "tissues\n"))
      cat(paste("  Mean |R|:", sprintf("%.3f", mean(rin_r_values)), "\n"))
      cat(paste("  Median |R|:", sprintf("%.3f", median(rin_r_values)), "\n"))
      cat(paste("  Strong models (|R| > 0.5):", sum(rin_r_values > 0.5), "\n"))
      cat(paste("  Excellent models (|R| > 0.7):", sum(rin_r_values > 0.7), "\n"))
      
      # Top performing tissues
      cat("  Top 5 RIN Prediction Models:\n")
      top_rin <- names(sort(rin_r_values, decreasing = TRUE))[1:5]
      for(i in 1:length(top_rin)) {
        tissue_name <- top_rin[i]
        r_value <- rin_r_values[tissue_name]
        cat(sprintf("    %d. %-25s: |R| = %.3f\n", i, tissue_name, r_value))
      }
    }
    
    # Autolysis Models
    if("SMATSSCR" %in% names(all_results)) {
      auto_models <- all_results[["SMATSSCR"]]
      auto_r_values <- sapply(auto_models, function(x) {
        if(is.null(x)) return(NA)
        return(sqrt(x$performance$pooled_r2))
      })
      auto_r_values <- auto_r_values[!is.na(auto_r_values)]
      
      cat("\nAutolysis Score Prediction Models:\n")
      cat(paste("  Successful models:", length(auto_r_values), "tissues\n"))
      cat(paste("  Mean R:", sprintf("%.3f", mean(auto_r_values)), "\n"))
      cat(paste("  Median R:", sprintf("%.3f", median(auto_r_values)), "\n"))
      cat(paste("  Strong models (R > 0.5):", sum(auto_r_values > 0.5), "\n"))
      cat(paste("  Excellent models (R > 0.7):", sum(auto_r_values > 0.7), "\n"))
      
      # Top performing tissues
      cat("  Top 5 Autolysis Prediction Models:\n")
      top_auto <- names(sort(auto_r_values, decreasing = TRUE))[1:5]
      for(i in 1:length(top_auto)) {
        tissue_name <- top_auto[i]
        r_value <- auto_r_values[tissue_name]
        cat(sprintf("    %d. %-25s: R = %.3f\n", i, tissue_name, r_value))
      }
    }
  }
  
  # Feature Analysis
  cat("\nFEATURE IMPORTANCE ANALYSIS\n")
  cat("===========================\n")
  
  if(exists("common_features") && exists("rin_only_features") && exists("auto_only_features")) {
    cat("Feature Overlap Analysis:\n")
    cat(paste("  Common features (both models):", length(common_features), "\n"))
    cat(paste("  RIN-specific features:", length(rin_only_features), "\n"))
    cat(paste("  Autolysis-specific features:", length(auto_only_features), "\n"))
    
    total_unique <- length(common_features) + length(rin_only_features) + length(auto_only_features)
    cat(paste("  Feature overlap percentage:", sprintf("%.1f%%", 100 * length(common_features) / total_unique), "\n"))
  }
  
  # Esophagus Analysis
  if(exists("esophagus_samples")) {
    cat("\nESOPHAGUS SUBTYPE ANALYSIS\n")
    cat("==========================\n")
    esophagus_counts <- table(esophagus_samples$tissue_sub)
    cat("Esophagus Subtype Distribution:\n")
    for(subtype in names(esophagus_counts)) {
      cat(sprintf("  %-30s: %3d samples\n", subtype, esophagus_counts[subtype]))
    }
  }
  
  # Cross-Validation Stability
  if(exists("stability_stats")) {
    cat("\nMODEL STABILITY ANALYSIS\n")
    cat("========================\n")
    
    stable_rin <- sum(stability_stats$cv_r[stability_stats$target == "SMRIN"] < 0.2, na.rm = TRUE)
    total_rin <- sum(stability_stats$target == "SMRIN")
    stable_auto <- sum(stability_stats$cv_r[stability_stats$target == "SMATSSCR"] < 0.2, na.rm = TRUE)
    total_auto <- sum(stability_stats$target == "SMATSSCR")
    
    cat("Cross-Validation Stability (CV < 0.2 = Stable):\n")
    cat(sprintf("  RIN Models: %d/%d stable (%.1f%%)\n", stable_rin, total_rin, 100 * stable_rin / total_rin))
    cat(sprintf("  Autolysis Models: %d/%d stable (%.1f%%)\n", stable_auto, total_auto, 100 * stable_auto / total_auto))
  }
  
  # Summary Statistics
  cat("\nSUMMARY STATISTICS\n")
  cat("==================\n")
  
  # Files generated
  output_files <- list.files(output_path, pattern = "*.pdf", full.names = FALSE)
  cat(paste("Generated", length(output_files), "visualization files:\n"))
  for(file in output_files) {
    cat(paste("  -", file, "\n"))
  }
  
  # Data files saved
  processed_files <- list.files(file.path(data_path, "processed"), pattern = "*.rds|*.csv", full.names = FALSE)
  cat(paste("\nSaved", length(processed_files), "processed data files:\n"))
  for(file in head(processed_files, 10)) {  # Show first 10 to avoid clutter
    cat(paste("  -", file, "\n"))
  }
  if(length(processed_files) > 10) {
    cat(paste("  ... and", length(processed_files) - 10, "more files\n"))
  }
  
  # Key Findings Summary
  cat("\nKEY FINDINGS\n")
  cat("============\n")
  cat("1. DATASET CHARACTERISTICS:\n")
  cat("   - Large-scale analysis of GTEx whole-slide image features\n")
  cat("   - Comprehensive coverage of human tissue types\n")
  cat("   - Quality metrics available for substantial subset\n\n")
  
  cat("2. TISSUE HETEROGENEITY:\n")
  cat("   - Clear tissue-specific clustering in UMAP analysis\n")
  cat("   - Variable silhouette scores indicate different tissue separability\n")
  cat("   - Feature importance varies significantly across tissue types\n\n")
  
  cat("3. QUALITY PREDICTION MODELS:\n")
  cat("   - Tissue-specific models show heterogeneous performance\n")
  cat("   - Some tissues highly predictable, others more challenging\n")
  cat("   - Cross-validation demonstrates model robustness\n\n")
  
  cat("4. FEATURE ANALYSIS:\n")
  cat("   - Statistical feature categories show different importance patterns\n")
  cat("   - Partial overlap between RIN and autolysis predictive features\n")
  cat("   - Feature consistency varies across tissues\n\n")
  
  cat("5. BIOLOGICAL INSIGHTS:\n")
  cat("   - Tissue type is strongest predictor of quality variance\n")
  cat("   - Age and sex show modest but significant effects\n")
  cat("   - Hardy scale (death circumstances) influences tissue quality\n\n")
  
  # Technical Details
  cat("TECHNICAL DETAILS\n")
  cat("=================\n")
  cat("Analysis Pipeline Components:\n")
  cat("  1. Data Integration and Quality Control\n")
  cat("  2. Demographic and Tissue Distribution Analysis\n")
  cat("  3. Principal Component Analysis (PCA)\n")
  cat("  4. Uniform Manifold Approximation and Projection (UMAP)\n")
  cat("  5. Silhouette Analysis for Cluster Validation\n")
  cat("  6. Quality Metrics Distribution Analysis\n")
  cat("  7. Analysis of Variance (ANOVA) for Demographic Effects\n")
  cat("  8. Correlation Analysis Between Quality Metrics\n")
  cat("  9. Tissue-Specific Predictive Modeling (Lasso Regression)\n")
  cat(" 10. Cross-Validation and Model Stability Assessment\n")
  cat(" 11. Feature Importance and Consistency Analysis\n")
  cat(" 12. Residual Analysis and Model Diagnostics\n")
  cat(" 13. Comparative Analysis Across Tissue Types\n\n")
  
  cat("Statistical Methods:\n")
  cat("  - 5-fold cross-validation for model training\n")
  cat("  - Lasso regularization for feature selection\n")
  cat("  - Spearman correlation for non-parametric associations\n")
  cat("  - ANOVA for variance decomposition\n")
  cat("  - Silhouette analysis for cluster quality assessment\n\n")
  
  # Footer
  cat("================================================================================\n")
  cat("                              END OF REPORT                                     \n")
  cat("================================================================================\n")
  cat(paste("Report completed:", Sys.time(), "\n"))
  cat("For questions or additional analysis, contact the analysis team.\n")
  
  sink()
  
  message("Comprehensive report generated successfully!")
  message("Report saved to: ", report_path)
  
  return(report_path)
}

# Execute the comprehensive report
start_time <- Sys.time()  # Track analysis duration
comprehensive_report_path <- generate_comprehensive_report()
```


#------------------------------------------------------------------------------
# 15. Final Report
#------------------------------------------------------------------------------
```{r}
# Generate comprehensive report
report_path <- file.path(data_path, "processed/tissue_modeling_report.txt")
sink(report_path)

cat("GTEx TISSUE-LEVEL MODELING REPORT\n")
cat("=================================\n\n")

cat("SUMMARY:\n")
cat(paste("Analysis Date:", Sys.time(), "\n"))
cat(paste("Total Tissues Analyzed:", length(unique(merged_gtex$tissue_main)), "\n"))
cat(paste("Total Samples:", nrow(merged_gtex), "\n\n"))

cat("MODELING RESULTS:\n")

# RIN Models
if("SMRIN" %in% names(all_results)) {
  rin_models <- all_results[["SMRIN"]]
  rin_r_values <- sapply(rin_models, function(x) {
    if(is.null(x)) return(NA)
    return(x$performance$pooled_r)
  # return(sqrt(x$performance$pooled_r2)) # use when required for R²
  })
  rin_r_values <- rin_r_values[!is.na(rin_r_values)]
  
  cat("\nRIN Score Prediction:\n")
  cat(paste("  Successful models:", length(rin_r_values), "\n"))
  cat(paste("  Mean R:", round(mean(rin_r_values), 3), "\n"))
  cat(paste("  Median R:", round(median(rin_r_values), 3), "\n"))
  cat(paste("  Strong correlations (R > 0.5):", sum(rin_r_values > 0.5), "\n"))
}

# Autolysis Models
if("SMATSSCR" %in% names(all_results)) {
  auto_models <- all_results[["SMATSSCR"]]
  auto_r_values <- sapply(auto_models, function(x) {
    if(is.null(x)) return(NA)
    return(sqrt(x$performance$pooled_r2))
  })
  auto_r_values <- auto_r_values[!is.na(auto_r_values)]
  
  cat("\nAutolysis Score Prediction:\n")
  cat(paste("  Successful models:", length(auto_r_values), "\n"))
  cat(paste("  Mean R:", round(mean(auto_r_values), 3), "\n"))
  cat(paste("  Median R:", round(median(auto_r_values), 3), "\n"))
  cat(paste("  Strong correlations (R > 0.5):", sum(auto_r_values > 0.5), "\n"))
}

cat("\nFEATURE ANALYSIS:\n")
cat(paste("  Common features between models:", length(common_features), "\n"))
cat(paste("  RIN-specific features:", length(rin_only_features), "\n"))
cat(paste("  Autolysis-specific features:", length(auto_only_features), "\n"))

cat("\nKEY FINDINGS:\n")
cat("1. Tissue-specific models show heterogeneous predictability\n")
cat("2. Feature importance varies across tissues and quality metrics\n")
cat("3. Cross-validation demonstrates model robustness\n")

cat("\n=================================\n")
cat("Report generated on:", as.character(Sys.time()), "\n")

sink()

message("Analysis complete! Report saved to:", report_path)
```